Robotic systems for determining a roll of a medical device in luminal networks

ABSTRACT

Certain aspects relate to systems and techniques for navigation-assisted medical devices. Some aspects relate to correlating features of depth information generated based on captured images of an anatomical luminal network with virtual features of depth information generated based on virtual images of a virtual representation of the anatomical luminal network in order to automatically determine aspects of a roll of a medical device within the luminal network.

CROSS-REFERENCE TO RELATED APPLICATION(S)

The present application is a continuation of U.S. patent applicationSer. No. 17/138,183, filed on Dec. 30, 2020, which is a continuation ofU.S. patent application Ser. No. 16/228,542, filed on Dec. 20, 2018, nowU.S. Pat. No. 11,278,357, which is a continuation of U.S. patentapplication Ser. No. 16/023,877, filed on Jun. 29, 2018, now U.S. Pat.No. 10,159,532, which is a continuation of U.S. patent application Ser.No. 15/631,691, filed on Jun. 23, 2017, now U.S. Pat. No. 10,022,192,the content of each of these applications of which is herebyincorporated by reference herein in its entirety.

TECHNICAL FIELD

The systems and methods disclosed herein are directed to medicalprocedures, and more particularly to navigation-assisted medicaldevices.

BACKGROUND

Medical procedures such as endoscopy (e.g., bronchoscopy) may involveaccessing and visualizing the inside of a patient's lumen (e.g.,airways) for diagnostic and/or therapeutic purposes. During a procedure,a flexible tubular tool such as, for example, an endoscope, may beinserted into the patient's body and an instrument can be passed throughthe endoscope to a tissue site identified for diagnosis and/ortreatment.

Bronchoscopy is a medical procedure that allows a physician to examinethe inside conditions of a patient's lung airways, such as bronchi andbronchioles. During the medical procedure, a thin, flexible tubulartool, known as a bronchoscope, may be inserted into the patient's mouthand passed down the patient's throat into his/her lung airways towards atissue site identified for subsequent diagnosis and treatment. Thebronchoscope can have an interior lumen (a “working channel”) providinga pathway to the tissue site, and catheters and various medical toolscan be inserted through the working channel to the tissue site.

SUMMARY

An endoscopy navigation system can use a fusion of different sensingmodalities (e.g., scope imaging data, electromagnetic (EM) positiondata, robotic position data, etc.) modeled, for example, throughadaptively-adjusted probabilities. A probabilistic navigation approachor other navigation approach may depend on an initial estimate of“where” the tip of the endoscope is—for example, an estimate of whichairway, how deep into this airway, and how much roll in this airway—inorder to begin tracking the tip of the endoscope. Some endoscopytechniques can involve a three-dimensional (3D) model of a patient'sanatomy, and can guide navigation using an EM field and positionsensors. At the outset of a procedure, the precise alignment (e.g.,registration) between the virtual space of the 3D model, the physicalspace of the patient's anatomy represented by the 3D model, and the EMfield may be unknown. As such, prior to generating a registration or insituations where the accuracy of an existing registration is inquestion, endoscope positions within the patient's anatomy cannot bemapped with precision to corresponding locations within the 3D model.

Typically, a navigation system requires the physician to undergo aseries of initialization steps in order to generate this initialestimate. This can involve, for example, instructing the physician toposition a bronchoscope at a number of specific positions andorientations relative to landmark(s) within the bronchial tree (e.g., bytouching the main carina, the left carina, and the right carina).Another option requires the physician to perform an initial airwaysurvey, for example, starting in the mid-trachea and entering each lobewhile attempting to maintain a centered position of the bronchoscope tipwithin each airway.

Such initialization steps can provide an initial estimate of theendoscope position; however, such an approach may have several potentialdrawbacks including adding additional time requirements to the beginningof the procedure. Another potential drawback relates to the fact that,after the initialization has been completed and tracking is occurring,an adverse event (e.g., patient coughing, dynamic airway collapse) cancreate uncertainty about the actual position of the endoscope. This cannecessitate determination of a new “initial” position, and accordinglythe navigation system may require the physician to navigate back to thetrachea to re-perform the initialization steps. Such backtracking addsadditional time requirements that can be particularly burdensome if theadverse event occurs after the endoscope has been navigated through thesmaller peripheral airways toward a target site.

The aforementioned issues, among others, are addressed by the luminalnetwork navigation systems and techniques described herein. Thedisclosed techniques can generate a 3D model of a virtual luminalnetwork representing the patient's anatomical luminal network and candetermine a number of locations within the virtual luminal network atwhich to position a virtual camera. The disclosed techniques cangenerate a virtual depth map representing distances between the internalsurfaces of the virtual luminal network and the virtual camerapositioned at a determined location. Features can be extracted fromthese virtual depth maps, for example, peak-to-peak distance in the caseof a virtual depth map representing an airway bifurcation, and theextracted features can be stored in association with the location of thevirtual camera. During the medical procedure, the distal end of anendoscope can be provided with an imaging device, and the disclosednavigation techniques can generate a depth map based on image datareceived from the imaging devices. The disclosed techniques can derivefeatures from the generated depth map, calculate correspondence betweenthe extracted features with the stored features extracted from one ofthe virtual depth maps, and then use the associated virtual cameralocation as the initial position of the distal end of the instrument.Beneficially, such techniques allow a probabilistic navigation system(or other navigation systems) to obtain an initial estimate of scopeposition without requiring the manual initialization steps describedabove. In addition, the disclosed techniques can be used throughout aprocedure to refine registration and, in some embodiments, can providean additional “initial estimate” after an adverse event withoutrequiring navigation back through the luminal network to a landmarkanatomical feature.

Accordingly, one aspect relates to a method of facilitating navigationof an anatomical luminal network of a patient, the method, executed by aset of one or more computing devices, comprising receiving imaging datacaptured by an imaging device at a distal end of an instrumentpositioned within the anatomical luminal network; accessing a virtualfeature derived from a virtual image simulated from a viewpoint of avirtual imaging device positioned at a virtual location within a virtualluminal network representative of the anatomical luminal network;calculating a correspondence between a feature derived from the imagingdata and the virtual feature derived from the virtual image; anddetermining a pose of the distal end of the instrument within theanatomical luminal network based on the virtual location associated withthe virtual feature.

In some embodiments, the method further comprises generating a depth mapbased on the imaging data, wherein the virtual feature is derived from avirtual depth map associated with the virtual image, and whereincalculating the correspondence is based at least partly on correlatingone or more features of the depth map and one or more features of thevirtual depth map.

In some embodiments, the method further comprises generating the depthmap by calculating, for each pixel of a plurality of pixels of theimaging data, a depth value representing an estimated distance betweenthe imaging device and a tissue surface within the anatomical luminalnetwork corresponding to the pixel; identifying a first pixel of theplurality of pixels corresponding to a first depth criterion in thedepth map and a second pixel of the plurality of pixels corresponding toa second depth criterion in the depth map; calculating a first valuerepresenting a distance between the first and second pixels; wherein thevirtual depth map comprises, for each virtual pixel of a plurality ofvirtual pixels, a virtual depth value representing a virtual distancebetween the virtual imaging device and a portion of the virtual luminalnetwork represented by the virtual pixel, and wherein accessing thevirtual feature derived from the virtual image comprises accessing asecond value representing a distance between first and second depthcriteria in the virtual depth map; and calculating the correspondencebased on comparing the first value to the second value.

In some embodiments, the method further comprises accessing a pluralityof values representing distances between first and second depth criteriain a plurality of virtual depth maps each representing a different oneof a plurality of virtual locations within the virtual luminal network;and calculating the correspondence based on the second valuecorresponding more closely to the first value than other values of theplurality of values. In some embodiments the anatomical luminal networkcomprises airways and the imaging data depicts a bifurcation of theairways, and the method further comprises identifying one of the firstand second depth criteria as a right bronchus in each of the depth mapand the virtual depth map; and determining a roll of the instrumentbased on an angular distance between a first position of the rightbronchus in the depth map and a second position of the right bronchus inthe virtual depth map, wherein the pose of the distal end of theinstrument within the anatomical luminal network comprises thedetermined roll.

In some embodiments, the method further comprises identifying three ormore depth criteria in each of the depth map and the virtual depth map;determining a shape and location of a polygon connecting the depthcriteria in each of the depth map and the virtual depth map; andcalculating the correspondence based on comparing the shape and locationof the polygon of the depth map to the shape and location of the polygonof the virtual depth map. In some embodiments, generating the depth mapis based on photoclinometry.

In some embodiments, the method further comprises calculating aprobabilistic state of the instrument within the anatomical luminalnetwork based on a plurality of inputs comprising the position; andguiding navigation of the instrument through the anatomical luminalnetwork based at least partly on the probabilistic state. In someembodiments, the method further comprises initializing a navigationsystem configured to calculate the probabilistic state and guide thenavigation of the anatomical luminal network based on the probabilisticstate, wherein the initializing of the navigation system comprisessetting a prior of a probability calculator based on the position. Insome embodiments, the method further comprises receiving additional datarepresenting an updated pose of the distal end of the instrument;setting a likelihood function of the probability calculator based on theadditional data; and determining the probabilistic state using theprobability calculator based on the prior and the likelihood function.

In some embodiments, the method further comprises providing theplurality of inputs to a navigation system configured to calculate theprobabilistic state, a first input comprising the pose of the distal endof the instrument and at least one additional input comprising one orboth of robotic position data from a robotic system actuating movementof the instrument and data received from a position sensor at the distalend of the instrument; and calculating the probabilistic state of theinstrument based on the first input and the at least one additionalinput.

In some embodiments, the method further comprises determining aregistration between a coordinate frame of the virtual luminal networkand a coordinate frame of an electromagnetic field generated around theanatomical luminal network based at least partly on the pose of thedistal end of the instrument within the anatomical luminal networkdetermined based on the calculated correspondence. In some embodiments,determining the position comprises determining a distance that thedistal end of the instrument is advanced within a segment of theanatomical luminal network.

Another aspect relates to a system configured to facilitate navigationof an anatomical luminal network of a patient, the system comprising animaging device at a distal end of an instrument; at least onecomputer-readable memory having stored thereon executable instructions;and one or more processors in communication with the at least onecomputer-readable memory and configured to execute the instructions tocause the system to at least receive imaging data captured by theimaging device with the distal end of the instrument positioned withinthe anatomical luminal network; access a virtual feature derived from avirtual image simulated from a viewpoint of a virtual imaging devicepositioned at a virtual location within a virtual luminal networkrepresentative of the anatomical luminal network; calculate acorrespondence between a feature derived from the imaging data and thevirtual feature derived from the virtual image; and determine a pose ofthe distal end of the instrument relative within the anatomical luminalnetwork based on the virtual location associated with the virtualfeature.

In some embodiments, the one or more processors are configured toexecute the instructions to cause the system to at least generate adepth map based on the imaging data, wherein the virtual imagerepresents a virtual depth map; and determine the correspondence basedat least partly on correlating one or more features of the depth map andone or more features of the virtual depth map. In some embodiments, theone or more processors are configured to execute the instructions tocause the system to at least generate the depth map by calculating, foreach pixel of a plurality of pixels of the imaging data, a depth valuerepresenting an estimated distance between the imaging device and atissue surface within the anatomical luminal network corresponding tothe pixel; identify a first pixel of the plurality of pixelscorresponding to a first depth criterion in the depth map and a secondpixel of the plurality of pixels corresponding to a second depthcriterion in the depth map; calculate a first value representing adistance between the first and second pixels; wherein the virtual depthmap comprises, for each virtual pixel of a plurality of virtual pixels,a virtual depth value representing a virtual distance between thevirtual imaging device and a portion of the virtual luminal networkrepresented by the virtual pixel, and wherein the feature derived fromthe virtual image comprises a second value representing a distancebetween first and second depth criteria in the virtual depth map; anddetermine the correspondence based on comparing the first value to thesecond value.

In some embodiments, the one or more processors are configured toexecute the instructions to cause the system to at least access aplurality of values representing distances between first and seconddepth criteria in a plurality of virtual depth maps each representing adifferent one of a plurality of virtual locations within the virtualluminal network; and calculate the correspondence based on the secondvalue corresponding more closely to the first value than other values ofthe plurality of values identify the second value as a closest match tothe first value among the plurality of values. In some embodiments, theanatomical luminal network comprises airways and the imaging datadepicts a bifurcation of the airways, and the one or more processors areconfigured to execute the instructions to cause the system to at leastidentify one of the first and second depth criteria as a right bronchusin each of the depth map and the virtual depth map; and determine a rollof the instrument based on an angular distance between a first positionof the right bronchus in the depth map and a second position of theright bronchus in the virtual depth map, wherein the pose of the distalend of the instrument within the anatomical luminal network comprisesthe determined roll.

In some embodiments, the one or more processors are configured toexecute the instructions to cause the system to at least identify threeor more depth criteria in each of the depth map and the virtual depthmap; determine a shape and location of a polygon connecting the three ormore depth criteria in each of the depth map and the virtual depth map;and calculate the correspondence based on comparing the shape andlocation of the polygon of the depth map to the shape and location ofthe polygon of the virtual depth map. In some embodiments, the one ormore processors are configured to execute the instructions to cause thesystem to at least generate the depth map based on photoclinometry.

In some embodiments, the one or more processors are configured tocommunicate with a navigation system, and wherein the one or moreprocessors are configured to execute the instructions to cause thesystem to at least calculate a probabilistic state of the instrumentwithin the anatomical luminal network using the navigation system basedat least partly on a plurality of inputs comprising the position; andguide navigation of the instrument through the anatomical luminalnetwork based at least partly on the probabilistic state calculated bythe navigation system. Some embodiments of the system further comprise arobotic system configured to guide movements of the instrument duringthe navigation. In some embodiments, the plurality of inputs compriserobotic position data received from the robotic system, and wherein theone or more processors are configured to execute the instructions tocause the system to at least calculate the probabilistic state of theinstrument using the navigation system based at least partly on theposition and on the robotic position data. Some embodiments of thesystem further compirse a position sensor at the distal end of aninstrument, the plurality of inputs comprise data received from theposition sensor, and wherein the one or more processors are configuredto execute the instructions to cause the system to at least calculatethe probabilistic state of the instrument using the navigation systembased at least partly on the position and on the data received from theposition sensor. In some embodiments, the one or more processors areconfigured to execute the instructions to cause the system to at leastdetermine a registration between a coordinate frame of the virtualluminal network and a coordinate frame of an electromagnetic fieldgenerated around the anatomical luminal network based at least partly onthe position.

Another aspect relates to a non-transitory computer readable storagemedium having stored thereon instructions that, when executed, cause atleast one computing device to at least access a virtualthree-dimensional model of internal surfaces of an anatomical luminalnetwork of a patient; identify a plurality of virtual locations withinthe virtual three-dimensional model; for each virtual location of theplurality of virtual locations within the virtual three-dimensionalmodel generate a virtual depth map representing virtual distancesbetween a virtual imaging device positioned at the virtual location anda portion of the internal surfaces within a field of view of the virtualimaging device when positioned at the virtual location, and derive atleast one virtual feature from the virtual depth map; and generate adatabase associating the plurality of virtual locations with the atleast one virtual feature derived from the corresponding virtual depthmap.

In some embodiments the instructions, when executed, cause the at leastone computing device to at least provide the database to a navigationsystem configured to guide navigation of an instrument through theanatomical luminal network during a medical procedure. In someembodiments the instructions, when executed, cause the at least onecomputing device to at least access data representing an imaging devicepositioned at a distal end of the instrument; identify image captureparameters of the imaging device; and set virtual image captureparameters of the virtual imaging device to correspond to the imagecapture parameters of the imaging device.

In some embodiments the instructions, when executed, cause the at leastone computing device to at least generate the virtual depth maps basedon the virtual image capture parameters. In some embodiments the imagecapture parameters comprise one or more of field of view, lensdistortion, focal length, and brightness shading.

In some embodiments the instructions, when executed, cause the at leastone computing device to at least for each virtual location of theplurality of virtual locations identify first and second depth criteriain the virtual depth map, and calculate a value representing a distancebetween the first and second depth criteria; and create the database byassociating the plurality of virtual locations with the correspondingvalue.

In some embodiments the instructions, when executed, cause the at leastone computing device to at least for each virtual location of theplurality of virtual locations identify three or more depth criteria inthe virtual depth map, and determine a shape and location of a polygonconnecting the three or more depth criteria; and create the database byassociating the plurality of virtual locations with the shape andlocation of the corresponding polygon. In some embodiments theinstructions, when executed, cause the at least one computing device toat least generate a three-dimensional volume of data from a series oftwo-dimensional images representing the anatomical luminal network ofthe patient; and form the virtual three-dimensional model of theinternal surfaces of the anatomical luminal network from thethree-dimensional volume of data. In some embodiments the instructions,when executed, cause the at least one computing device to at leastcontrol a computed tomography imaging system to capture the series oftwo-dimensional images. In some embodiments the instructions, whenexecuted, cause the at least one computing device to at least form thevirtual three-dimensional model by applying volume segmentation to thethree-dimensional volume of data.

Another aspect relates to a method of facilitating navigation of ananatomical luminal network of a patient, the method, executed by a setof one or more computing devices, comprising receiving a stereoscopicimage set representing an interior of the anatomical luminal network;generating a depth map based on the stereoscopic image set; accessing avirtual feature derived from a virtual image simulated from a viewpointof a virtual imaging device positioned at a location within a virtualluminal network; calculating a correspondence between a feature derivedfrom the depth map and the virtual feature derived from the virtualimage; and determining a pose of the distal end of the instrument withinthe anatomical luminal network based on the virtual location ofassociated with the virtual feature.

In some embodiments, generating the stereoscopic image set comprisespositioning an imaging device at a distal end of an instrument at afirst location within the anatomical luminal network; capturing a firstimage of an interior of the anatomical luminal network with the imagingdevice positioned at the first location; robotically controlling theimaging device to move a known distance to a second location within theanatomical luminal network; and capturing a second image of the interiorof the anatomical luminal network with the imaging device positioned atthe second location. In some embodiments, robotically controlling theimaging device to move the known distance comprises one or both ofretracting the imaging device and angularly rolling the imaging device.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosed aspects will hereinafter be described in conjunction withthe appended drawings and appendices, provided to illustrate and not tolimit the disclosed aspects, wherein like designations denote likeelements.

FIG. 1 illustrates an embodiment of a cart-based robotic system arrangedfor diagnostic and/or therapeutic bronchoscopy procedure(s).

FIG. 2 depicts further aspects of the robotic system of FIG. 1 .

FIG. 3 illustrates an embodiment of the robotic system of FIG. 1arranged for ureteroscopy.

FIG. 4 illustrates an embodiment of the robotic system of FIG. 1arranged for a vascular procedure.

FIG. 5 illustrates an embodiment of a table-based robotic systemarranged for a bronchoscopy procedure.

FIG. 6 provides an alternative view of the robotic system of FIG. 5 .

FIG. 7 illustrates an example system configured to stow robotic arm(s).

FIG. 8 illustrates an embodiment of a table-based robotic systemconfigured for a ureteroscopy procedure.

FIG. 9 illustrates an embodiment of a table-based robotic systemconfigured for a laparoscopic procedure.

FIG. 10 illustrates an embodiment of the table-based robotic system ofFIGS. 5-9 with pitch or tilt adjustment.

FIG. 11 provides a detailed illustration of the interface between thetable and the column of the table-based robotic system of FIGS. 5-10 .

FIG. 12 illustrates an exemplary instrument driver.

FIG. 13 illustrates an exemplary medical instrument with a pairedinstrument driver.

FIG. 14 illustrates an alternative design for an instrument driver andinstrument where the axes of the drive units are parallel to the axis ofthe elongated shaft of the instrument.

FIG. 15 depicts a block diagram illustrating a localization system thatestimates a location of one or more elements of the robotic systems ofFIGS. 1-10 , such as the location of the instrument of FIGS. 13-14 , inaccordance to an example embodiment.

FIG. 16A illustrates an example operating environment implementing thedisclosed navigation systems and techniques.

FIG. 16B illustrates an example luminal network navigated in theenvironment of FIG. 16A.

FIG. 16C illustrates an example robotic arm for guiding instrumentmovement in through the luminal network of FIG. 16B.

FIG. 17 illustrates an example command console for the example medicalrobotic system, according to one embodiment.

FIG. 18 illustrates an example endoscope having imaging and EM sensingcapabilities as described herein.

FIG. 19 depicts a schematic block diagram of a navigation system asdescribed herein.

FIG. 20 depicts a flowchart of an example process for generating anextracted virtual feature data set.

FIG. 21 depicts a flowchart of an example intra-operative process forgenerating depth information based on captured endoscopic images andcalculated correspondence between features of the depth information withthe extracted virtual feature data set of FIG. 20 .

DETAILED DESCRIPTION 1. Overview

Aspects of the present disclosure may be integrated into arobotically-enabled medical system capable of performing a variety ofmedical procedures, including both minimally invasive, such aslaparoscopy, and non-invasive, such as endoscopy, procedures. Amongendoscopy procedures, the system may be capable of performingbronchoscopy, ureteroscopy, gastroenterology, etc.

In addition to performing the breadth of procedures, the system mayprovide additional benefits, such as enhanced imaging and guidance toassist the physician. Additionally, the system may provide the physicianwith the ability to perform the procedure from an ergonomic positionwithout the need for awkward arm motions and positions. Still further,the system may provide the physician with the ability to perform theprocedure with improved ease of use such that one or more of theinstruments of the system can be controlled by a single user.

Various embodiments will be described below in conjunction with thedrawings for purposes of illustration. It should be appreciated thatmany other implementations of the disclosed concepts are possible, andvarious advantages can be achieved with the disclosed implementations.Headings are included herein for reference and to aid in locatingvarious sections. These headings are not intended to limit the scope ofthe concepts described with respect thereto. Such concepts may haveapplicability throughout the entire specification.

A. Robotic System—Cart.

The robotically-enabled medical system may be configured in a variety ofways depending on the particular procedure. FIG. 1 illustrates anembodiment of a cart-based robotically-enabled system 10 arranged for adiagnostic and/or therapeutic bronchoscopy procedure. During abronchoscopy, the system 10 may comprise a cart 11 having one or morerobotic arms 12 to deliver a medical instrument, such as a steerableendoscope 13, which may be a procedure-specific bronchoscope forbronchoscopy, to a natural orifice access point (i.e., the mouth of thepatient positioned on a table in the present example) to deliverdiagnostic and/or therapeutic tools. As shown, the cart 11 may bepositioned proximate to the patient's upper torso in order to provideaccess to the access point. Similarly, the robotic arms 12 may beactuated to position the bronchoscope relative to the access point. Thearrangement in FIG. 1 may also be utilized when performing agastro-intestinal (GI) procedure with a gastroscope, a specializedendoscope for GI procedures. FIG. 2 depicts an example embodiment of thecart in greater detail.

With continued reference to FIG. 1 , once the cart 11 is properlypositioned, the robotic arms 12 may insert the steerable endoscope 13into the patient robotically, manually, or a combination thereof. Asshown, the steerable endoscope 13 may comprise at least two telescopingparts, such as an inner leader portion and an outer sheath portion, eachportion coupled to a separate instrument driver from the set ofinstrument drivers 28, each instrument driver coupled to the distal endof an individual robotic arm. This linear arrangement of the instrumentdrivers 28, which facilitates coaxially aligning the leader portion withthe sheath portion, creates a “virtual rail” 29 that may be repositionedin space by manipulating the one or more robotic arms 12 into differentangles and/or positions. The virtual rails described herein are depictedin the Figures using dashed lines, and accordingly the dashed lines donot depict any physical structure of the system. Translation of theinstrument drivers 28 along the virtual rail 29 telescopes the innerleader portion relative to the outer sheath portion or advances orretracts the endoscope 13 from the patient. The angle of the virtualrail 29 may be adjusted, translated, and pivoted based on clinicalapplication or physician preference. For example, in bronchoscopy, theangle and position of the virtual rail 29 as shown represents acompromise between providing physician access to the endoscope 13 whileminimizing friction that results from bending the endoscope 13 into thepatient's mouth.

The endoscope 13 may be directed down the patient's trachea and lungsafter insertion using precise commands from the robotic system untilreaching the target destination or operative site. In order to enhancenavigation through the patient's lung network and/or reach the desiredtarget, the endoscope 13 may be manipulated to telescopically extend theinner leader portion from the outer sheath portion to obtain enhancedarticulation and greater bend radius. The use of separate instrumentdrivers 28 also allows the leader portion and sheath portion to bedriven independent of each other.

For example, the endoscope 13 may be directed to deliver a biopsy needleto a target, such as, for example, a lesion or nodule within the lungsof a patient. The needle may be deployed down a working channel thatruns the length of the endoscope to obtain a tissue sample to beanalyzed by a pathologist. Depending on the pathology results,additional tools may be deployed down the working channel of theendoscope for additional biopsies. After identifying a nodule to bemalignant, the endoscope 13 may endoscopically deliver tools to resectthe potentially cancerous tissue. In some instances, diagnostic andtherapeutic treatments may need to be delivered in separate procedures.In those circumstances, the endoscope 13 may also be used to deliver afiducial to “mark” the location of the target nodule as well. In otherinstances, diagnostic and therapeutic treatments may be delivered duringthe same procedure.

The system 10 may also include a movable tower 30, which may beconnected via support cables to the cart 11 to provide support forcontrols, electronics, fluidics, optics, sensors, and/or power to thecart 11. Placing such functionality in the tower 30 allows for a smallerform factor cart 11 that may be more easily adjusted and/orre-positioned by an operating physician and his/her staff. Additionally,the division of functionality between the cart/table and the supporttower 30 reduces operating room clutter and facilitates improvingclinical workflow. While the cart 11 may be positioned close to thepatient, the tower 30 may be stowed in a remote location to stay out ofthe way during a procedure.

In support of the robotic systems described above, the tower 30 mayinclude component(s) of a computer-based control system that storescomputer program instructions, for example, within a non-transitorycomputer-readable storage medium such as a persistent magnetic storagedrive, solid state drive, etc. The execution of those instructions,whether the execution occurs in the tower 30 or the cart 11, may controlthe entire system or sub-system(s) thereof. For example, when executedby a processor of the computer system, the instructions may cause thecomponents of the robotics system to actuate the relevant carriages andarm mounts, actuate the robotics arms, and control the medicalinstruments. For example, in response to receiving the control signal,the motors in the joints of the robotics arms may position the arms intoa certain posture.

The tower 30 may also include a pump, flow meter, valve control, and/orfluid access in order to provide controlled irrigation and aspirationcapabilities to system that may be deployed through the endoscope 13.These components may also be controlled using the computer system oftower 30. In some embodiments, irrigation and aspiration capabilitiesmay be delivered directly to the endoscope 13 through separate cable(s).

The tower 30 may include a voltage and surge protector designed toprovide filtered and protected electrical power to the cart 11, therebyavoiding placement of a power transformer and other auxiliary powercomponents in the cart 11, resulting in a smaller, more moveable cart11.

The tower 30 may also include support equipment for the sensors deployedthroughout the robotic system 10. For example, the tower 30 may includeopto-electronics equipment for detecting, receiving, and processing datareceived from the optical sensors or cameras throughout the roboticsystem 10. In combination with the control system, such opto-electronicsequipment may be used to generate real-time images for display in anynumber of consoles deployed throughout the system, including in thetower 30. Similarly, the tower 30 may also include an electronicsubsystem for receiving and processing signals received from deployedelectromagnetic (EM) sensors. The tower 30 may also be used to house andposition an EM field generator for detection by EM sensors in or on themedical instrument.

The tower 30 may also include a console 31 in addition to other consolesavailable in the rest of the system, e.g., console mounted on top of thecart. The console 31 may include a user interface and a display screen,such as a touchscreen, for the physician operator. Consoles in system 10are generally designed to provide both robotic controls as well aspre-operative and real-time information of the procedure, such asnavigational and localization information of the endoscope 13. When theconsole 31 is not the only console available to the physician, it may beused by a second operator, such as a nurse, to monitor the health orvitals of the patient and the operation of system, as well as provideprocedure-specific data, such as navigational and localizationinformation.

The tower 30 may be coupled to the cart 11 and endoscope 13 through oneor more cables or connections (not shown). In some embodiments, thesupport functionality from the tower 30 may be provided through a singlecable to the cart 11, simplifying and de-cluttering the operating room.In other embodiments, specific functionality may be coupled in separatecabling and connections. For example, while power may be providedthrough a single power cable to the cart, the support for controls,optics, fluidics, and/or navigation may be provided through a separatecable.

FIG. 2 provides a detailed illustration of an embodiment of the cartfrom the cart-based robotically-enabled system shown in FIG. 1 . Thecart 11 generally includes an elongated support structure 14 (oftenreferred to as a “column”), a cart base 15, and a console 16 at the topof the column 14. The column 14 may include one or more carriages, suchas a carriage 17 (alternatively “arm support”) for supporting thedeployment of one or more robotic arms 12 (three shown in FIG. 2 ). Thecarriage 17 may include individually configurable arm mounts that rotatealong a perpendicular axis to adjust the base of the robotic arms 12 forbetter positioning relative to the patient. The carriage 17 alsoincludes a carriage interface 19 that allows the carriage 17 tovertically translate along the column 14.

The carriage interface 19 is connected to the column 14 through slots,such as slot 20, that are positioned on opposite sides of the column 14to guide the vertical translation of the carriage 17. The slot 20contains a vertical translation interface to position and hold thecarriage at various vertical heights relative to the cart base 15.Vertical translation of the carriage 17 allows the cart 11 to adjust thereach of the robotic arms 12 to meet a variety of table heights, patientsizes, and physician preferences. Similarly, the individuallyconfigurable arm mounts on the carriage 17 allow the robotic arm base 21of robotic arms 12 to be angled in a variety of configurations.

In some embodiments, the slot 20 may be supplemented with slot coversthat are flush and parallel to the slot surface to prevent dirt andfluid ingress into the internal chambers of the column 14 and thevertical translation interface as the carriage 17 vertically translates.The slot covers may be deployed through pairs of spring spoolspositioned near the vertical top and bottom of the slot 20. The coversare coiled within the spools until deployed to extend and retract fromtheir coiled state as the carriage 17 vertically translates up and down.The spring-loading of the spools provides force to retract the coverinto a spool when carriage 17 translates towards the spool, while alsomaintaining a tight seal when the carriage 17 translates away from thespool. The covers may be connected to the carriage 17 using, forexample, brackets in the carriage interface 19 to ensure properextension and retraction of the cover as the carriage 17 translates.

The column 14 may internally comprise mechanisms, such as gears andmotors, that are designed to use a vertically aligned lead screw totranslate the carriage 17 in a mechanized fashion in response to controlsignals generated in response to user inputs, e.g., inputs from theconsole 16.

The robotic arms 12 may generally comprise robotic arm bases 21 and endeffectors 22, separated by a series of linkages 23 that are connected bya series of joints 24, each joint comprising an independent actuator,each actuator comprising an independently controllable motor. Eachindependently controllable joint represents an independent degree offreedom available to the robotic arm. Each of the arms 12 have sevenjoints, and thus provide seven degrees of freedom. A multitude of jointsresult in a multitude of degrees of freedom, allowing for “redundant”degrees of freedom. Redundant degrees of freedom allow the robotic arms12 to position their respective end effectors 22 at a specific position,orientation, and trajectory in space using different linkage positionsand joint angles. This allows for the system to position and direct amedical instrument from a desired point in space while allowing thephysician to move the arm joints into a clinically advantageous positionaway from the patient to create greater access, while avoiding armcollisions.

The cart base 15 balances the weight of the column 14, carriage 17, andarms 12 over the floor. Accordingly, the cart base 15 houses heaviercomponents, such as electronics, motors, power supply, as well ascomponents that either enable movement and/or immobilize the cart. Forexample, the cart base 15 includes rollable wheel-shaped casters 25 thatallow for the cart to easily move around the room prior to a procedure.After reaching the appropriate position, the casters 25 may beimmobilized using wheel locks to hold the cart 11 in place during theprocedure.

Positioned at the vertical end of column 14, the console 16 allows forboth a user interface for receiving user input and a display screen (ora dual-purpose device such as, for example, a touchscreen 26) to providethe physician user with both pre-operative and intra-operative data.Potential pre-operative data on the touchscreen 26 may includepre-operative plans, navigation and mapping data derived frompre-operative computerized tomography (CT) scans, and/or notes frompre-operative patient interviews. Intra-operative data on display mayinclude optical information provided from the tool, sensor andcoordinate information from sensors, as well as vital patientstatistics, such as respiration, heart rate, and/or pulse. The console16 may be positioned and tilted to allow a physician to access theconsole from the side of the column 14 opposite carriage 17. From thisposition the physician may view the console 16, robotic arms 12, andpatient while operating the console 16 from behind the cart 11. Asshown, the console 16 also includes a handle 27 to assist withmaneuvering and stabilizing cart 11.

FIG. 3 illustrates an embodiment of a robotically-enabled system 10arranged for ureteroscopy. In a ureteroscopic procedure, the cart 11 maybe positioned to deliver a ureteroscope 32, a procedure-specificendoscope designed to traverse a patient's urethra and ureter, to thelower abdominal area of the patient. In a ureteroscopy, it may bedesirable for the ureteroscope 32 to be directly aligned with thepatient's urethra to reduce friction and forces on the sensitive anatomyin the area. As shown, the cart 11 may be aligned at the foot of thetable to allow the robotic arms 12 to position the ureteroscope 32 fordirect linear access to the patient's urethra. From the foot of thetable, the robotic arms 12 may insert ureteroscope 32 along the virtualrail 33 directly into the patient's lower abdomen through the urethra.

After insertion into the urethra, using similar control techniques as inbronchoscopy, the ureteroscope 32 may be navigated into the bladder,ureters, and/or kidneys for diagnostic and/or therapeutic applications.For example, the ureteroscope 32 may be directed into the ureter andkidneys to break up kidney stone build up using laser or ultrasoniclithotripsy device deployed down the working channel of the ureteroscope32. After lithotripsy is complete, the resulting stone fragments may beremoved using baskets deployed down the ureteroscope 32.

FIG. 4 illustrates an embodiment of a robotically-enabled systemsimilarly arranged for a vascular procedure. In a vascular procedure,the system 10 may be configured such the cart 11 may deliver a medicalinstrument 34, such as a steerable catheter, to an access point in thefemoral artery in the patient's leg. The femoral artery presents both alarger diameter for navigation as well as relatively less circuitous andtortuous path to the patient's heart, which simplifies navigation. As ina ureteroscopic procedure, the cart 11 may be positioned towards thepatient's legs and lower abdomen to allow the robotic arms 12 to providea virtual rail 35 with direct linear access to the femoral artery accesspoint in the patient's thigh/hip region. After insertion into theartery, the medical instrument 34 may be directed and inserted bytranslating the instrument drivers 28. Alternatively, the cart may bepositioned around the patient's upper abdomen in order to reachalternative vascular access points, such as, for example, the carotidand brachial arteries near the shoulder and wrist.

B. Robotic System—Table.

Embodiments of the robotically-enabled medical system may alsoincorporate the patient's table. Incorporation of the table reduces theamount of capital equipment within the operating room by removing thecart, which allows greater access to the patient. FIG. 5 illustrates anembodiment of such a robotically-enabled system arranged for abronchoscopy procedure. System 36 includes a support structure or column37 for supporting platform 38 (shown as a “table” or “bed”) over thefloor. Much like in the cart-based systems, the end effectors of therobotic arms 39 of the system 36 comprise instrument drivers 42 that aredesigned to manipulate an elongated medical instrument, such as abronchoscope 40 in FIG. 5 , through or along a virtual rail 41 formedfrom the linear alignment of the instrument drivers 42. In practice, aC-arm for providing fluoroscopic imaging may be positioned over thepatient's upper abdominal area by placing the emitter and detectoraround table 38.

FIG. 6 provides an alternative view of the system 36 without the patientand medical instrument for discussion purposes. As shown, the column 37may include one or more carriages 43 shown as ring-shaped in the system36, from which the one or more robotic arms 39 may be based. Thecarriages 43 may translate along a vertical column interface 44 thatruns the length of the column 37 to provide different vantage pointsfrom which the robotic arms 39 may be positioned to reach the patient.The carriage(s) 43 may rotate around the column 37 using a mechanicalmotor positioned within the column 37 to allow the robotic arms 39 tohave access to multiples sides of the table 38, such as, for example,both sides of the patient. In embodiments with multiple carriages, thecarriages may be individually positioned on the column and may translateand/or rotate independent of the other carriages. While carriages 43need not surround the column 37 or even be circular, the ring-shape asshown facilitates rotation of the carriages 43 around the column 37while maintaining structural balance. Rotation and translation of thecarriages 43 allows the system to align the medical instruments, such asendoscopes and laparoscopes, into different access points on thepatient.

The arms 39 may be mounted on the carriages through a set of arm mounts45 comprising a series of joints that may individually rotate and/ortelescopically extend to provide additional configurability to therobotic arms 39. Additionally, the arm mounts 45 may be positioned onthe carriages 43 such that, when the carriages 43 are appropriatelyrotated, the arm mounts 45 may be positioned on either the same side oftable 38 (as shown in FIG. 6 ), on opposite sides of table 38 (as shownin FIG. 9 ), or on adjacent sides of the table 38 (not shown).

The column 37 structurally provides support for the table 38, and a pathfor vertical translation of the carriages. Internally, the column 37 maybe equipped with lead screws for guiding vertical translation of thecarriages, and motors to mechanize the translation of said carriagesbased the lead screws. The column 37 may also convey power and controlsignals to the carriage 43 and robotic arms 39 mounted thereon.

The table base 46 serves a similar function as the cart base 15 in cart11 shown in FIG. 2 , housing heavier components to balance the table/bed38, the column 37, the carriages 43, and the robotic arms 39. The tablebase 46 may also incorporate rigid casters to provide stability duringprocedures. Deployed from the bottom of the table base 46, the castersmay extend in opposite directions on both sides of the base 46 andretract when the system 36 needs to be moved.

Continuing with FIG. 6 , the system 36 may also include a tower (notshown) that divides the functionality of system 36 between table andtower to reduce the form factor and bulk of the table. As in earlierdisclosed embodiments, the tower may be provide a variety of supportfunctionalities to table, such as processing, computing, and controlcapabilities, power, fluidics, and/or optical and sensor processing. Thetower may also be movable to be positioned away from the patient toimprove physician access and de-clutter the operating room.Additionally, placing components in the tower allows for more storagespace in the table base for potential stowage of the robotic arms. Thetower may also include a console that provides both a user interface foruser input, such as keyboard and/or pendant, as well as a display screen(or touchscreen) for pre-operative and intra-operative information, suchas real-time imaging, navigation, and tracking information.

In some embodiments, a table base may stow and store the robotic armswhen not in use. FIG. 7 illustrates a system 47 that stows robotic armsin an embodiment of the table-based system. In system 47, carriages 48may be vertically translated into base 49 to stow robotic arms 50, armmounts 51, and the carriages 48 within the base 49. Base covers 52 maybe translated and retracted open to deploy the carriages 48, arm mounts51, and arms 50 around column 53, and closed to stow to protect themwhen not in use. The base covers 52 may be sealed with a membrane 54along the edges of its opening to prevent dirt and fluid ingress whenclosed.

FIG. 8 illustrates an embodiment of a robotically-enabled table-basedsystem configured for a ureteroscopy procedure. In a ureteroscopy, thetable 38 may include a swivel portion 55 for positioning a patientoff-angle from the column 37 and table base 46. The swivel portion 55may rotate or pivot around a pivot point (e.g., located below thepatient's head) in order to position the bottom portion of the swivelportion 55 away from the column 37. For example, the pivoting of theswivel portion 55 allows a C-arm (not shown) to be positioned over thepatient's lower abdomen without competing for space with the column (notshown) below table 38. By rotating the carriage 35 (not shown) aroundthe column 37, the robotic arms 39 may directly insert a ureteroscope 56along a virtual rail 57 into the patient's groin area to reach theurethra. In a ureteroscopy, stirrups 58 may also be fixed to the swivelportion 55 of the table 38 to support the position of the patient's legsduring the procedure and allow clear access to the patient's groin area.

In a laparoscopic procedure, through small incision(s) in the patient'sabdominal wall, minimally invasive instruments (elongated in shape toaccommodate the size of the one or more incisions) may be inserted intothe patient's anatomy. After inflation of the patient's abdominalcavity, the instruments, often referred to as laparoscopes, may bedirected to perform surgical tasks, such as grasping, cutting, ablating,suturing, etc. FIG. 9 illustrates an embodiment of a robotically-enabledtable-based system configured for a laparoscopic procedure. As shown inFIG. 9 , the carriages 43 of the system 36 may be rotated and verticallyadjusted to position pairs of the robotic arms 39 on opposite sides ofthe table 38, such that laparoscopes 59 may be positioned using the armmounts 45 to be passed through minimal incisions on both sides of thepatient to reach his/her abdominal cavity.

To accommodate laparoscopic procedures, the robotically-enabled tablesystem may also tilt the platform to a desired angle. FIG. 10illustrates an embodiment of the robotically-enabled medical system withpitch or tilt adjustment. As shown in FIG. 10 , the system 36 mayaccommodate tilt of the table 38 to position one portion of the table ata greater distance from the floor than the other. Additionally, the armmounts 45 may rotate to match the tilt such that the arms 39 maintainthe same planar relationship with table 38. To accommodate steeperangles, the column 37 may also include telescoping portions 60 thatallow vertical extension of column 37 to keep the table 38 from touchingthe floor or colliding with base 46.

FIG. 11 provides a detailed illustration of the interface between thetable 38 and the column 37. Pitch rotation mechanism 61 may beconfigured to alter the pitch angle of the table 38 relative to thecolumn 37 in multiple degrees of freedom. The pitch rotation mechanism61 may be enabled by the positioning of orthogonal axes 1, 2 at thecolumn-table interface, each axis actuated by a separate motor 2, 4responsive to an electrical pitch angle command. Rotation along onescrew 5 would enable tilt adjustments in one axis 1, while rotationalong the other screw 6 would enable tilt adjustments along the otheraxis 2.

For example, pitch adjustments are particularly useful when trying toposition the table in a Trendelenburg position, i.e., position thepatient's lower abdomen at a higher position from the floor than thepatient's lower abdomen, for lower abdominal surgery. The Trendelenburgposition causes the patient's internal organs to slide towards his/herupper abdomen through the force of gravity, clearing out the abdominalcavity for minimally invasive tools to enter and perform lower abdominalsurgical procedures, such as laparoscopic prostatectomy.

C. Instrument Driver & Interface.

The end effectors of the system's robotic arms comprise (i) aninstrument driver (alternatively referred to as “instrument drivemechanism” or “instrument device manipulator”) that incorporateelectro-mechanical means for actuating the medical instrument and (ii) aremovable or detachable medical instrument which may be devoid of anyelectro-mechanical components, such as motors. This dichotomy may bedriven by the need to sterilize medical instruments used in medicalprocedures, and the inability to adequately sterilize expensive capitalequipment due to their intricate mechanical assemblies and sensitiveelectronics. Accordingly, the medical instruments may be designed to bedetached, removed, and interchanged from the instrument driver (and thusthe system) for individual sterilization or disposal by the physician orthe physician's staff. In contrast, the instrument drivers need not bechanged or sterilized, and may be draped for protection.

FIG. 12 illustrates an example instrument driver. Positioned at thedistal end of a robotic arm, instrument driver 62 comprises of one ormore drive units 63 arranged with parallel axes to provide controlledtorque to a medical instrument via drive shafts 64. Each drive unit 63comprises an individual drive shaft 64 for interacting with theinstrument, a gear head 65 for converting the motor shaft rotation to adesired torque, a motor 66 for generating the drive torque, an encoder67 to measure the speed of the motor shaft and provide feedback to thecontrol circuitry, and control circuitry 68 for receiving controlsignals and actuating the drive unit. Each drive unit 63 beingindependent controlled and motorized, the instrument driver 62 mayprovide multiple (four as shown in FIG. 12 ) independent drive outputsto the medical instrument. In operation, the control circuitry 68 wouldreceive a control signal, transmit a motor signal to the motor 66,compare the resulting motor speed as measured by the encoder 67 with thedesired speed, and modulate the motor signal to generate the desiredtorque.

For procedures that require a sterile environment, the robotic systemmay incorporate a drive interface, such as a sterile adapter connectedto a sterile drape, that sits between the instrument driver and themedical instrument. The chief purpose of the sterile adapter is totransfer angular motion from the drive shafts of the instrument driverto the drive inputs of the instrument while maintaining physicalseparation, and thus sterility, between the drive shafts and driveinputs. Accordingly, an example sterile adapter may comprise of a seriesof rotational inputs and outputs intended to be mated with the driveshafts of the instrument driver and drive inputs on the instrument.Connected to the sterile adapter, the sterile drape, comprised of athin, flexible material such as transparent or translucent plastic, isdesigned to cover the capital equipment, such as the instrument driver,robotic arm, and cart (in a cart-based system) or table (in atable-based system). Use of the drape would allow the capital equipmentto be positioned proximate to the patient while still being located inan area not requiring sterilization (i.e., non-sterile field). On theother side of the sterile drape, the medical instrument may interfacewith the patient in an area requiring sterilization (i.e., sterilefield).

D. Medical Instrument.

FIG. 13 illustrates an example medical instrument with a pairedinstrument driver. Like other instruments designed for use with arobotic system, medical instrument 70 comprises an elongated shaft 71(or elongate body) and an instrument base 72. The instrument base 72,also referred to as an “instrument handle” due to its intended designfor manual interaction by the physician, may generally compriserotatable drive inputs 73, e.g., receptacles, pulleys or spools, thatare designed to be mated with drive outputs 74 that extend through adrive interface on instrument driver 75 at the distal end of robotic arm76. When physically connected, latched, and/or coupled, the mated driveinputs 73 of instrument base 72 may share axes of rotation with thedrive outputs 74 in the instrument driver 75 to allow the transfer oftorque from drive outputs 74 to drive inputs 73. In some embodiments,the drive outputs 74 may comprise splines that are designed to mate withreceptacles on the drive inputs 73.

The elongated shaft 71 is designed to be delivered through either ananatomical opening or lumen, e.g., as in endoscopy, or a minimallyinvasive incision, e.g., as in laparoscopy. The elongated shaft 66 maybe either flexible (e.g., having properties similar to an endoscope) orrigid (e.g., having properties similar to a laparoscope) or contain acustomized combination of both flexible and rigid portions. Whendesigned for laparoscopy, the distal end of a rigid elongated shaft maybe connected to an end effector comprising a jointed wrist formed from aclevis with an axis of rotation and a surgical tool, such as, forexample, a grasper or scissors, that may be actuated based on force fromthe tendons as the drive inputs rotate in response to torque receivedfrom the drive outputs 74 of the instrument driver 75. When designed forendoscopy, the distal end of a flexible elongated shaft may include asteerable or controllable bending section that may be articulated andbent based on torque received from the drive outputs 74 of theinstrument driver 75.

Torque from the instrument driver 75 is transmitted down the elongatedshaft 71 using tendons within the shaft 71. These individual tendons,such as pull wires, may be individually anchored to individual driveinputs 73 within the instrument handle 72. From the handle 72, thetendons are directed down one or more pull lumens within the elongatedshaft 71 and anchored at the distal portion of the elongated shaft 71.In laparoscopy, these tendons may be coupled to a distally mounted endeffector, such as a wrist, grasper, or scissor. Under such anarrangement, torque exerted on drive inputs 73 would transfer tension tothe tendon, thereby causing the end effector to actuate in some way. Inlaparoscopy, the tendon may cause a joint to rotate about an axis,thereby causing the end effector to move in one direction or another.Alternatively, the tendon may be connected to one or more jaws of agrasper at distal end of the elongated shaft 71, where tension from thetendon cause the grasper to close.

In endoscopy, the tendons may be coupled to a bending or articulatingsection positioned along the elongated shaft 71 (e.g., at the distalend) via adhesive, control ring, or other mechanical fixation. Whenfixedly attached to the distal end of a bending section, torque exertedon drive inputs 73 would be transmitted down the tendons, causing thesofter, bending section (sometimes referred to as the articulablesection or region) to bend or articulate. Along the non-bendingsections, it may be advantageous to spiral or helix the individual pulllumens that direct the individual tendons along (or inside) the walls ofthe endoscope shaft to balance the radial forces that result fromtension in the pull wires. The angle of the spiraling and/or spacingthere between may be altered or engineered for specific purposes,wherein tighter spiraling exhibits lesser shaft compression under loadforces, while lower amounts of spiraling results in greater shaftcompression under load forces, but also exhibits limits bending. On theother end of the spectrum, the pull lumens may be directed parallel tothe longitudinal axis of the elongated shaft 71 to allow for controlledarticulation in the desired bending or articulable sections.

In endoscopy, the elongated shaft 71 houses a number of components toassist with the robotic procedure. The shaft may comprise of a workingchannel for deploying surgical tools, irrigation, and/or aspiration tothe operative region at the distal end of the shaft 71. The shaft 71 mayalso accommodate wires and/or optical fibers to transfer signals to/froman optical assembly at the distal tip, which may include of an opticalcamera. The shaft 71 may also accommodate optical fibers to carry lightfrom proximally-located light sources, such as light emitting diodes, tothe distal end of the shaft.

At the distal end of the instrument 70, the distal tip may also comprisethe opening of a working channel for delivering tools for diagnosticand/or therapy, irrigation, and aspiration to an operative site. Thedistal tip may also include a port for a camera, such as a fiberscope ora digital camera, to capture images of an internal anatomical space.Relatedly, the distal tip may also include ports for light sources forilluminating the anatomical space when using the camera.

In the example of FIG. 13 , the drive shaft axes, and thus the driveinput axes, are orthogonal to the axis of the elongated shaft. Thisarrangement, however, complicates roll capabilities for the elongatedshaft 71. Rolling the elongated shaft 71 along its axis while keepingthe drive inputs 73 static results in undesirable tangling of thetendons as they extend off the drive inputs 73 and enter pull lumenswithin the elongate shaft 71. The resulting entanglement of such tendonsmay disrupt any control algorithms intended to predict movement of theflexible elongate shaft during an endoscopic procedure.

FIG. 14 illustrates an alternative design for an instrument driver andinstrument where the axes of the drive units are parallel to the axis ofthe elongated shaft of the instrument. As shown, a circular instrumentdriver 80 comprises four drive units with their drive outputs 81 alignedin parallel at the end of a robotic arm 82. The drive units, and theirrespective drive outputs 81, are housed in a rotational assembly 83 ofthe instrument driver 80 that is driven by one of the drive units withinthe assembly 83. In response to torque provided by the rotational driveunit, the rotational assembly 83 rotates along a circular bearing thatconnects the rotational assembly 83 to the non-rotational portion 84 ofthe instrument driver. Power and controls signals may be communicatedfrom the non-rotational portion 84 of the instrument driver 80 to therotational assembly 83 through electrical contacts may be maintainedthrough rotation by a brushed slip ring connection (not shown). In otherembodiments, the rotational assembly 83 may be responsive to a separatedrive unit that is integrated into the non-rotatable portion 84, andthus not in parallel to the other drive units. The rotational mechanism83 allows the instrument driver 80 to rotate the drive units, and theirrespective drive outputs 81, as a single unit around an instrumentdriver axis 85.

Like earlier disclosed embodiments, an instrument 86 may comprise of anelongated shaft portion 88 and an instrument base 87 (shown with atransparent external skin for discussion purposes) comprising aplurality of drive inputs 89 (such as receptacles, pulleys, and spools)that are configured to receive the drive outputs 81 in the instrumentdriver 80. Unlike prior disclosed embodiments, instrument shaft 88extends from the center of instrument base 87 with an axis substantiallyparallel to the axes of the drive inputs 89, rather than orthogonal asin the design of FIG. 13 .

When coupled to the rotational assembly 83 of the instrument driver 80,the medical instrument 86, comprising instrument base 87 and instrumentshaft 88, rotates in combination with the rotational assembly 83 aboutthe instrument driver axis 85. Since the instrument shaft 88 ispositioned at the center of instrument base 87, the instrument shaft 88is coaxial with instrument driver axis 85 when attached. Thus, rotationof the rotational assembly 83 causes the instrument shaft 88 to rotateabout its own longitudinal axis. Moreover, as the instrument base 87rotates with the instrument shaft 88, any tendons connected to the driveinputs 89 in the instrument base 87 are not tangled during rotation.Accordingly, the parallelism of the axes of the drive outputs 81, driveinputs 89, and instrument shaft 88 allows for the shaft rotation withouttangling any control tendons.

E. Navigation and Control.

Traditional endoscopy may involve the use of fluoroscopy (e.g., as maybe delivered through a C-arm) and other forms of radiation-based imagingmodalities to provide endoluminal guidance to an operator physician. Incontrast, the robotic systems contemplated by this disclosure canprovide for non-radiation-based navigational and localization means toreduce physician exposure to radiation and reduce the amount ofequipment within the operating room. As used herein, the term“localization” may refer to determining and/or monitoring the positionof objects in a reference coordinate system. Technologies such aspre-operative mapping, computer vision, real-time EM tracking, and robotcommand data may be used individually or in combination to achieve aradiation-free operating environment. In other cases, whereradiation-based imaging modalities are still used, the pre-operativemapping, computer vision, real-time EM tracking, and robot command datamay be used individually or in combination to improve upon theinformation obtained solely through radiation-based imaging modalities.

FIG. 15 is a block diagram illustrating a localization system 90 thatestimates a location of one or more elements of the robotic system, suchas the location of the instrument, in accordance to an exampleembodiment. The localization system 90 may be a set of one or morecomputer devices configured to execute one or more instructions. Thecomputer devices may be embodied by a processor (or processors) andcomputer-readable memory in one or more components discussed above. Byway of example and not limitation, the computer devices may be in thetower 30 shown in FIG. 1 , the cart shown in FIGS. 1-4 , the beds shownin FIGS. 5-10 , etc.

As shown in FIG. 15 , the localization system 90 may include alocalization module 95 that processes input data 91-94 to generatelocation data 96 for the distal tip of a medical instrument. Thelocation data 96 may be data or logic that represents a location and/ororientation of the distal end of the instrument relative to a frame ofreference. The frame of reference can be a frame of reference relativeto the anatomy of the patient or to a known object, such as an EM fieldgenerator (see discussion below for the EM field generator).

The various input data 91-94 are now described in greater detail.Pre-operative mapping may be accomplished through the use of thecollection of low dose CT scans. Pre-operative CT scans generatetwo-dimensional images, each representing a “slice” of a cutaway view ofthe patient's internal anatomy. When analyzed in the aggregate,image-based models for anatomical cavities, spaces and structures of thepatient's anatomy, such as a patient lung network, may be generated.Techniques such as center-line geometry may be determined andapproximated from the CT images to develop a three-dimensional volume ofthe patient's anatomy, referred to as preoperative model data 91. Theuse of center-line geometry is discussed in U.S. patent application Ser.No. 14/523,760, the contents of which are herein incorporated in itsentirety. Network topological models may also be derived from theCT-images, and are particularly appropriate for bronchoscopy.

In some embodiments, the instrument may be equipped with a camera toprovide vision data 92. The localization module 95 may process thevision data to enable one or more vision-based location tracking. Forexample, the preoperative model data may be used in conjunction with thevision data 92 to enable computer vision-based tracking of the medicalinstrument (e.g., an endoscope or an instrument advance through aworking channel of the endoscope). For example, using the preoperativemodel data 91, the robotic system may generate a library of expectedendoscopic images from the model based on the expected path of travel ofthe endoscope, each image linked to a location within the model.Intra-operatively, this library may be referenced by the robotic systemin order to compare real-time images captured at the camera (e.g., acamera at a distal end of the endoscope) to those in the image libraryto assist localization.

Other computer vision-based tracking techniques use feature tracking todetermine motion of the camera, and thus the endoscope. Some feature ofthe localization module 95 may identify circular geometries in thepreoperative model data 91 that correspond to anatomical lumens andtrack the change of those geometries to determine which anatomical lumenwas selected, as well as the relative rotational and/or translationalmotion of the camera. Use of a topological map may further enhancevision-based algorithms or techniques.

Optical flow, another computer vision-based technique, may analyze thedisplacement and translation of image pixels in a video sequence in thevision data 92 to infer camera movement. Through the comparison ofmultiple frames over multiple iterations, movement and location of thecamera (and thus the endoscope) may be determined.

The localization module 95 may use real-time EM tracking to generate areal-time location of the endoscope in a global coordinate system thatmay be registered to the patient's anatomy, represented by thepreoperative model. In EM tracking, an EM sensor (or tracker) comprisingof one or more sensor coils embedded in one or more locations andorientations in a medical instrument (e.g., an endoscopic tool) measuresthe variation in the EM field created by one or more static EM fieldgenerators positioned at a known location. The location informationdetected by the EM sensors is stored as EM data 93. The EM fieldgenerator (or transmitter), may be placed close to the patient to createa low intensity magnetic field that the embedded sensor may detect. Themagnetic field induces small currents in the sensor coils of the EMsensor, which may be analyzed to determine the distance and anglebetween the EM sensor and the EM field generator. These distances andorientations may be intra-operatively “registered” to the patientanatomy (e.g., the preoperative model) in order to determine thegeometric transformation that aligns a single location in the coordinatesystem with a position in the pre-operative model of the patient'sanatomy. Once registered, an embedded EM tracker in one or morepositions of the medical instrument (e.g., the distal tip of anendoscope) may provide real-time indications of the progression of themedical instrument through the patient's anatomy.

Robotic command and kinematics data 94 may also be used by thelocalization module 95 to provide localization data 96 for the roboticsystem. Device pitch and yaw resulting from articulation commands may bedetermined during pre-operative calibration. Intra-operatively, thesecalibration measurements may be used in combination with known insertiondepth information to estimate the position of the instrument.Alternatively, these calculations may be analyzed in combination withEM, vision, and/or topological modeling to estimate the position of themedical instrument within the network.

As FIG. 15 shows, a number of other input data can be used by thelocalization module 95. For example, although not shown in FIG. 15 , aninstrument utilizing shape-sensing fiber can provide shape data that thelocalization module 95 can use to determine the location and shape ofthe instrument.

The localization module 95 may use the input data 91-94 incombination(s). In some cases, such a combination may use aprobabilistic approach where the localization module 95 assigns aconfidence weight to the location determined from each of the input data91-94. Thus, where the EM data may not be reliable (as may be the casewhere there is EM interference) the confidence of the locationdetermined by the EM data 93 can be decrease and the localization module95 may rely more heavily on the vision data 92 and/or the roboticcommand and kinematics data 94.

As discussed above, the robotic systems discussed herein may be designedto incorporate a combination of one or more of the technologies above.The robotic system's computer-based control system, based in the tower,bed and/or cart, may store computer program instructions, for example,within a non-transitory computer-readable storage medium such as apersistent magnetic storage drive, solid state drive, or the like, that,upon execution, cause the system to receive and analyze sensor data anduser commands, generate control signals throughout the system, anddisplay the navigational and localization data, such as the position ofthe instrument within the global coordinate system, anatomical map, etc.

2. Introduction to Automatically-Initialized Navigation Systems

Embodiments of the disclosure relate to systems and techniques thatfacilitate navigation of a medical instrument through luminal networks,for example, lung airways or other anatomical structures having interioropen space, by generating and using depth information from endoscopeimages to determine an initial endoscope position, by analyzing multiplenavigation-related data sources to increase accuracy in estimation oflocation and orientation of a medical instrument within the luminalnetwork, and by generating and using additional depth information tore-initialize the navigation system after an adverse event.

A bronchoscope can include a light source and a small camera that allowsa physician to inspect a patient's windpipe and airways. Patient traumacan occur if the precise location of the bronchoscope within the patientairways is not known. To ascertain the location of the bronchoscope,image-based bronchoscopy guidance systems can use data from thebronchoscope camera to perform local registrations (e.g., registrationsat a particular location within a luminal network) at bifurcations ofpatient airways and so beneficially can be less susceptible to positionerrors due to patient breathing motion. However, as image-based guidancemethods rely on the bronchoscope video, they can be affected byartifacts in bronchoscope video caused by patient coughing or mucousobstruction, etc.

Electromagnetic navigation-guided bronchoscopy (EMN bronchoscopy) is atype of bronchoscopic procedure that implements EM technology tolocalize and guide endoscopic tools or catheters through the bronchialpathways of the lung. EMN bronchoscopy systems can use an EM fieldgenerator that emits a low-intensity, varying EM field and establishesthe position of the tracking volume around the luminal network of thepatient. The EM field is a physical field produced by electricallycharged objects that affects the behavior of charged objects in thevicinity of the field. EM sensors attached to objects positioned withinthe generated field can be used to track locations and orientations ofthese objects within the EM field. Small currents are induced in the EMsensors by the varying electromagnetic field. The characteristics ofthese electrical signals are dependent on the distance and angle betweena sensor and the EM field generator. Accordingly, an EMN bronchoscopysystem can include an EM field generator, a steerable medical instrumenthaving an EM sensor at or near its distal tip, and a guidance computingsystem. The EM field generator generates an EM field around the luminalnetwork of the patient to be navigated, for example, airways,gastrointestinal tract, or a circulatory pathway. The steerable channelis inserted through the working channel of the bronchoscope and trackedin the EM field via the EM sensor.

Prior to the start of an EMN bronchoscopy procedure, a virtual,three-dimensional (3D) bronchial map can be obtained for the patient'sspecific airway structure, for example, from a preoperative CT chestscan. Using the map and an EMN bronchoscopy system, physicians cannavigate to a desired location within the lung to biopsy lesions, stagelymph nodes, insert markers to guide radiotherapy or guide brachytherapycatheters. For example, a registration can be performed at the beginningof a procedure to generate a mapping between the coordinate system ofthe EM field and the model coordinate system. Thus, as the steerablechannel is tracked during bronchoscopy, the steerable channel's positionin the model coordinate system becomes nominally known based on positiondata from the EM sensor.

As used herein, a coordinate frame is the frame of reference of aparticular sensing modality. For example, for EM data the EM coordinateframe is the frame of reference defined by the source of the EM field(e.g., the field generator). For CT images and for a segmented 3D model,this frame of reference is based on the frame defined by the scanner.The present navigation systems address the problem of navigation ofrepresenting (register) these different sources of data (which are intheir own frames of reference) to the 3D model (i.e. the CT frame), forexample, in order to display the location of the instrument inside themodel.

Accordingly, as described in more detail below, the disclosed luminalnetwork navigation systems and techniques can combine input from bothimage-based navigation systems, robotic systems, and EM navigationsystems, as well as input from other patient sensors, in order tomitigate navigational problems and enable more effective endoscopyprocedures. For example, a navigation fusion system can analyze imageinformation received from an instrument camera, position informationfrom an EM sensor on the instrument tip, and robotic positioninformation from a robotic system guiding movement of the instrument.Based on the analysis, the navigation fusion framework can baseinstrument position estimates and/or navigation decisions on one or moreof these types of navigation data. Some implementations of thenavigation fusion framework can further determine instrument positionrelative to a 3D model of the luminal network. In some embodiments, theinitial instrument position used to initialize tracking via thenavigation fusion system can be generated based on depth information asdescribed herein.

The disclosed systems and techniques can provide advantages forbronchoscopy guidance systems and other applications, including othertypes of endoscopic procedures for navigation of luminal networks. Inanatomy, a “lumen” may refer to the inner open space or cavity of anorgan, as of an airway, a blood vessel, a kidney, a heart, an intestine,or any other suitable organ in which a medical procedure is beingperformed. As used herein, a “luminal network” refers to an anatomicalstructure having at least one lumen leading towards a target tissuesite, for example, the airways of the lungs, the circulatory system,calyx, and the gastrointestinal system. Thus, although the presentdisclosure provides examples of navigation systems relating tobronchoscopy, it will be appreciated that the disclosed positionestimation aspects are applicable to other medical systems fornavigation of a luminal network of a patient. As such, the disclosedsystems and techniques can be used with bronchoscopes, ureteroscopes,gastrointestinal endoscopes, and other suitable medical instruments.

3. Overview of Example Navigation Systems

FIG. 16A illustrates an example operating environment 100 implementingone or more aspects of the disclosed navigation systems and techniques.The operating environment 100 includes patient 101, a platform 102supporting the patient 101, a medical robotic system 110 guidingmovement of endoscope 115, command center 105 for controlling operationsof the medical robotic system 110, EM controller 135, EM field generator120, and EM sensors 125, 130. FIG. 16A also illustrates an outline of aregion of a luminal network 140 within the patient 101, shown in moredetail in FIG. 16B.

The medical robotic system 110 can include one or more robotic arms forpositioning and guiding movement of endoscope 115 through the luminalnetwork 140 of the patient 101. Command center 105 can becommunicatively coupled to the medical robotic system 110 for receivingposition data and/or providing control signals from a user. As usedherein, “communicatively coupled” refers to any wired and/or wirelessdata transfer mediums, including but not limited to a wireless wide areanetwork (WWAN) (e.g., one or more cellular networks), a wireless localarea network (WLAN) (e.g., configured for one or more standards, such asthe IEEE 802.11 (Wi-Fi)), Bluetooth, data transfer cables, and/or thelike. The medical robotic system 110 can be any of the systems describedabove with respect to FIGS. 1-15 . An embodiment of the medical roboticsystem 110 is discussed in more detail with respect to FIG. 16C, and thecommand center 105 is discussed in more detail with respect to FIG. 17 .

The endoscope 115 may be a tubular and flexible surgical instrument thatis inserted into the anatomy of a patient to capture images of theanatomy (e.g., body tissue) and provide a working channel for insertionof other medical instruments to a target tissue site. As describedabove, the endoscope 115 can be a procedure-specific endoscope, forexample a bronchoscope, gastroscope, or ureteroscope, or may be alaparoscope or vascular steerable catheter. The endoscope 115 caninclude one or more imaging devices (e.g., cameras or other types ofoptical sensors) at its distal end. The imaging devices may include oneor more optical components such as an optical fiber, fiber array,photosensitive substrate, and/or lens(es). The optical components movealong with the tip of the endoscope 115 such that movement of the tip ofthe endoscope 115 results in corresponding changes to the field of viewof the images captured by the imaging devices. The distal end of theendoscope 115 can be provided with one or more EM sensors 125 fortracking the position of the distal end within an EM field generatedaround the luminal network 140. The distal end of the endoscope 115 isfurther described with reference to FIG. 18 below.

EM controller 135 can control EM field generator 120 to produce avarying EM field. The EM field can be time-varying and/or spatiallyvarying, depending upon the embodiment. The EM field generator 120 canbe an EM field generating board in some embodiments. Some embodiments ofthe disclosed patient navigation systems can use an EM field generatorboard positioned between the patient and the platform 102 supporting thepatient, and the EM field generator board can incorporate a thin barrierthat minimizes any tracking distortions caused by conductive or magneticmaterials located below it. In other embodiments, an EM field generatorboard can be mounted on a robotic arm, for example, similar to thoseshown in medical robotic system 110, which can offer flexible setupoptions around the patient.

An EM spatial measurement system incorporated into the command center105, medical robotic system 110, and/or EM controller 135 can determinethe location of objects within the EM field that are embedded orprovided with EM sensor coils, for example, EM sensors 125, 130. When anEM sensor is placed inside a controlled, varying EM field as describedherein, voltages are induced in the sensor coils. These induced voltagescan be used by the EM spatial measurement system to calculate theposition and orientation of the EM sensor and thus the object having theEM sensor. As the magnetic fields are of a low field strength and cansafely pass through human tissue, location measurement of an object ispossible without the line-of-sight constraints of an optical spatialmeasurement system.

EM sensor 125 can be coupled to a distal end of the endoscope 115 inorder to track its location within the EM field. The EM field isstationary relative to the EM field generator, and a coordinate frame ofa 3D model of the luminal network can be mapped to a coordinate frame ofthe EM field. A number of additional EM sensors 130 can be provided onthe body surface of the patient (e.g., in the region of the luminalnetwork 140) in order to aid in tracking the location of the EM sensor125, for example, by enabling compensation for patient movementincluding displacement caused by respiration. A number of different EMsensors 130 can be spaced apart on the body surface.

FIG. 16B illustrates an example luminal network 140 that can benavigated in the operating environment 100 of FIG. 16A. The luminalnetwork 140 includes the branched structure of the airways 150 of thepatient, the trachea 154 leading to the main carina 156 (the firstbifurcation encountered during bronchoscopy navigation), and a nodule(or lesion) 155 that can be accessed as described herein for diagnosisand/or treatment. As illustrated, the nodule 155 is located at theperiphery of the airways 150. The endoscope 115 has a first diameter andthus its distal end is not able to be positioned through thesmaller-diameter airways around the nodule 155. Accordingly, a steerablecatheter 155 extends from the working channel of the endoscope 115 theremaining distance to the nodule 155. The steerable catheter 145 mayhave a lumen through which instruments, for example, biopsy needles,cytology brushes, and/or tissue sampling forceps, can be passed to thetarget tissue site of nodule 155. In such implementations, both thedistal end of the endoscope 115 and the distal end of the steerablecatheter 145 can be provided with EM sensors for tracking their positionwithin the airways 150. In other embodiments, the overall diameter ofthe endoscope 115 may be small enough to reach the periphery without thesteerable catheter 155, or may be small enough to get close to theperiphery (e.g., within 2.5-3 cm) to deploy medical instruments througha non-steerable catheter. The medical instruments deployed through theendoscope 115 may be equipped with EM sensors, and the positionestimation techniques described below can be applied to such medicalinstruments when they are deployed beyond the distal tip of theendoscope 115.

In some embodiments, a 2D display of a 3D luminal network model asdescribed herein, or a cross-section of a 3D model, can resemble FIG.16B. Estimated position information can be overlaid onto such arepresentation.

FIG. 16C illustrates an example robotic arm 175 of a medical roboticsystem 110 for guiding instrument movement in through the luminalnetwork 140 of FIG. 16B. The robotic arm 175 can be robotic arms 12, 39described above in some embodiments, and is coupled to base 180, whichcan be cart base 15, column 37 of patient platform 38, or aceiling-based mount in various embodiments. As described above, therobotic arm 175 includes multiple arm segments 170 coupled at joints165, which provides the robotic arm 175 multiple degrees of freedom.

The robotic arm 175 may be coupled to an instrument driver 190, forexample instrument driver 62 described above, using a mechanism changerinterface (MCI) 160. The instrument driver 190 can be removed andreplaced with a different type of instrument driver, for example, afirst type of instrument driver configured to manipulate an endoscope ora second type of instrument driver configured to manipulate alaparoscope. The MCI 160 includes connectors to transfer pneumaticpressure, electrical power, electrical signals, and optical signals fromthe robotic arm 175 to the instrument driver 190. The MCI 160 can be aset screw or base plate connector. The instrument driver 190 manipulatessurgical instruments, for example, the endoscope 115 using techniquesincluding direct drive, harmonic drive, geared drives, belts andpulleys, magnetic drives, and the like. The MCI 160 is interchangeablebased on the type of instrument driver 190 and can be customized for acertain type of surgical procedure. The robotic 175 arm can include ajoint level torque sensing and a wrist at a distal end.

Robotic arm 175 of the medical robotic system 110 can manipulate theendoscope 115 using tendons as described above to deflect the tip of theendoscope 115. The endoscope 115 may exhibit nonlinear behavior inresponse to forces applied by the elongate movement members. Thenonlinear behavior may be based on stiffness and compressibility of theendoscope 115, as well as variability in slack or stiffness betweendifferent elongate movement members.

The base 180 can be positioned such that the robotic arm 175 has accessto perform or assist with a surgical procedure on a patient, while auser such as a physician may control the medical robotic system 110 fromthe comfort of the command console. The base 180 can be communicativelycoupled to the command console 105 shown in FIG. 16A.

The base 180 can include a source of power 182, pneumatic pressure 186,and control and sensor electronics 184—including components such as acentral processing unit, data bus, control circuitry, and memory—andrelated actuators such as motors to move the robotic arm 175. Theelectronics 184 can implement the navigation control techniquesdescribed herein. The electronics 184 in the base 180 may also processand transmit control signals communicated from the command console. Insome embodiments, the base 180 includes wheels 188 to transport themedical robotic system 110 and wheel locks/brakes (not shown) for thewheels 188. Mobility of the medical robotic system 110 helps accommodatespace constraints in a surgical operating room as well as facilitateappropriate positioning and movement of surgical equipment. Further, themobility allows the robotic arm 175 to be configured such that therobotic arm 175 does not interfere with the patient, physician,anesthesiologist, or any other equipment. During procedures, a user maycontrol the robotic arm 175 using control devices, for example, thecommand console.

FIG. 17 illustrates an example command console 200 that can be used, forexample, as the command console 105 in the example operating environment100. The command console 200 includes a console base 201, displaymodules 202, e.g., monitors, and control modules, e.g., a keyboard 203and joystick 204. In some embodiments, one or more of the commandconsole 200 functionality may be integrated into a base 180 of themedical robotic system 110 or another system communicatively coupled tothe medical robotic system 110. A user 205, e.g., a physician, remotelycontrols the medical robotic system 110 from an ergonomic position usingthe command console 200.

The console base 201 may include a central processing unit, a memoryunit, a data bus, and associated data communication ports that areresponsible for interpreting and processing signals such as cameraimagery and tracking sensor data, e.g., from the endoscope 115 shown inFIGS. 16A-16C. In some embodiments, both the console base 201 and thebase 180 perform signal processing for load-balancing. The console base201 may also process commands and instructions provided by the user 205through the control modules 203 and 204. In addition to the keyboard 203and joystick 204 shown in FIG. 17 , the control modules may includeother devices, for example, computer mice, trackpads, trackballs,control pads, controllers such as handheld remote controllers, andsensors (e.g., motion sensors or cameras) that capture hand gestures andfinger gestures. A controller can include a set of user inputs (e.g.,buttons, joysticks, directional pads, etc.) mapped to an operation ofthe instrument (e.g., articulation, driving, water irrigation, etc.).

The user 205 can control a surgical instrument such as the endoscope 115using the command console 200 in a velocity mode or position controlmode. In velocity mode, the user 205 directly controls pitch and yawmotion of a distal end of the endoscope 115 based on direct manualcontrol using the control modules. For example, movement on the joystick204 may be mapped to yaw and pitch movement in the distal end of theendoscope 115. The joystick 204 can provide haptic feedback to the user205. For example, the joystick 204 may vibrate to indicate that theendoscope 115 cannot further translate or rotate in a certain direction.The command console 200 can also provide visual feedback (e.g., pop-upmessages) and/or audio feedback (e.g., beeping) to indicate that theendoscope 115 has reached maximum translation or rotation.

In position control mode, the command console 200 uses a 3D map of apatient luminal network and input from navigational sensors as describedherein to control a surgical instrument, e.g., the endoscope 115. Thecommand console 200 provides control signals to robotic arms 175 of themedical robotic system 110 to manipulate the endoscope 115 to a targetlocation. Due to the reliance on the 3D map, position control mode mayrequire accurate mapping of the anatomy of the patient.

In some embodiments, users 205 can manually manipulate robotic arms 175of the medical robotic system 110 without using the command console 200.During setup in a surgical operating room, the users 205 may move therobotic arms 175, endoscope 115 (or endoscopes), and other surgicalequipment to access a patient. The medical robotic system 110 may relyon force feedback and inertia control from the users 205 to determineappropriate configuration of the robotic arms 175 and equipment.

The displays 202 may include electronic monitors (e.g., LCD displays,LED displays, touch-sensitive displays), virtual reality viewingdevices, e.g., goggles or glasses, and/or other display devices. In someembodiments, the display modules 202 are integrated with the controlmodules, for example, as a tablet device with a touchscreen. In someembodiments, one of the displays 202 can display a 3D model of thepatient's luminal network and virtual navigation information (e.g., avirtual representation of the end of the endoscope within the modelbased on EM sensor position) while the other of the displays 202 candisplay image information received from the camera or another sensingdevice at the end of the endoscope 115. In some implementations, theuser 205 can both view data and input commands to the medical roboticsystem 110 using the integrated displays 202 and control modules. Thedisplays 202 can display 2D renderings of 3D images and/or 3D imagesusing a stereoscopic device, e.g., a visor or goggles. The 3D imagesprovide an “endo view” (i.e., endoscopic view), which is a computer 3Dmodel illustrating the anatomy of a patient. The “endo view” provides avirtual environment of the patient's interior and an expected locationof an endoscope 115 inside the patient. A user 205 compares the “endoview” model to actual images captured by a camera to help mentallyorient and confirm that the endoscope 115 is in the correct—orapproximately correct—location within the patient. The “endo view”provides information about anatomical structures, e.g., the shape ofairways, circulatory vessels, or an intestine or colon of the patient,around the distal end of the endoscope 115. The display modules 202 cansimultaneously display the 3D model and CT scans of the anatomy thearound distal end of the endoscope 115. Further, the display modules 202may overlay the already determined navigation paths of the endoscope 115on the 3D model and CT scans.

In some embodiments, a model of the endoscope 115 is displayed with the3D models to help indicate a status of a surgical procedure. Forexample, the CT scans identify a lesion in the anatomy where a biopsymay be necessary. During operation, the display modules 202 may show areference image captured by the endoscope 115 corresponding to thecurrent location of the endoscope 115. The display modules 202 mayautomatically display different views of the model of the endoscope 115depending on user settings and a particular surgical procedure. Forexample, the display modules 202 show an overhead fluoroscopic view ofthe endoscope 115 during a navigation step as the endoscope 115approaches an operative region of a patient.

FIG. 18 illustrates the distal end 300 of an example endoscope havingimaging and EM sensing capabilities as described herein, for example,the endoscope 115 of FIGS. 16A-16C. In FIG. 18 , the distal end 300 ofthe endoscope includes an imaging device 315, illumination sources 310,and ends of EM sensor coils 305. The distal end 300 further includes anopening to a working channel 320 of the endoscope through which surgicalinstruments, such as biopsy needles, cytology brushes, and forceps, maybe inserted along the endoscope shaft, allowing access to the area nearthe endoscope tip.

The illumination sources 310 provide light to illuminate a portion of ananatomical space. The illumination sources can each be one or morelight-emitting devices configured to emit light at a selected wavelengthor range of wavelengths. The wavelengths can be any suitable wavelength,for example, visible spectrum light, infrared light, x-ray (e.g., forfluoroscopy), to name a few examples. In some embodiments, illuminationsources 310 can include light-emitting diodes (LEDs) located at thedistal end 300. In some embodiments, illumination sources 310 caninclude one or more fiber optic fibers extending through a length of theendoscope to transmit light through the distal end 300 from a remotelight source, for example, an x-ray generator. Where the distal end 300includes multiple illumination sources 310 these can each be configuredto emit the same or different wavelengths of light as one another.

The imaging device 315 can include any photosensitive substrate orstructure configured to convert energy representing received light intoelectric signals, for example, a charge-coupled device (CCD) orcomplementary metal-oxide semiconductor (CMOS) image sensor. Someexamples of imaging device 315 can include one or more optical fibers,for example, a fiber optic bundle, configured to transmit lightrepresenting an image from the distal end 300 of the endoscope to aneyepiece and/or image sensor near the proximal end of the endoscope.Imaging device 315 can additionally include one or more lenses and/orwavelength pass or cutoff filters as required for various opticaldesigns. The light emitted from the illumination sources 310 allows theimaging device 315 to capture images of the interior of a patient'sluminal network. These images can then be transmitted as individualframes or series of successive frames (e.g., a video) to a computersystem such as command console 200 for processing as described herein.

Electromagnetic coils 305 located on the distal end 300 may be used withan electromagnetic tracking system to detect the position andorientation of the distal end 300 of the endoscope while it is disposedwithin an anatomical system. In some embodiments, the coils 305 may beangled to provide sensitivity to electromagnetic fields along differentaxes, giving the disclosed navigational systems the ability to measure afull 6 degrees of freedom: three positional and three angular. In otherembodiments, only a single coil may be disposed on or within the distalend 300 with its axis oriented along the endoscope shaft of theendoscope. Due to the rotational symmetry of such a system, it isinsensitive to roll about its axis, so only 5 degrees of freedom may bedetected in such an implementation.

FIG. 19 illustrates a schematic block diagram of an example navigationfusion system 400 as described herein. As described in more detailbelow, using the system 400, data from a number of different sources iscombined and repeatedly analyzed during a surgical procedure to providean estimation of the real-time movement information andlocation/orientation information of a surgical instrument (e.g., theendoscope) within the luminal network of the patient and to makenavigation decisions.

The navigation fusion system 400 includes a number of data repositoriesincluding depth features data repository 405, endoscope EM sensor datarepository 415, registration data repository 475, model data repository425, endoscope imaging data repository 480, navigation path datarepository 445, and robotic position data repository 470. Though shownseparately in FIG. 19 for purposes of clarity in the discussion below,it will be appreciated that some or all of the data repositories can bestored together in a single memory or set of memories. The system 400also includes a number of processing modules including a registrationcalculator 465, depth-based position estimator 410, location calculator430, image analyzer 435, state estimator 440, and navigation controller460. Each module can represent a set of computer-readable instructions,stored in a memory, and one or more processors configured by theinstructions for performing the features described below together. Thenavigation fusion system 400 can be implemented as one or more datastorage devices and one or more hardware processors, for example, in thecontrol and sensor electronics 184 and/or console base 201 describedabove. The navigation fusion system 400 can be an embodiment of thelocalization system 90 in some implementations.

FIG. 19 also illustrates modeling system 420 in communication with thenavigation fusion system 400. As described in more detail below, usingthe modeling system 420, data representing a number of images of apatient's anatomical luminal network can be analyzed to build athree-dimensional model of a virtual representation of the anatomicalluminal network, and this virtual anatomical luminal network can be usedto build the depth features data repository 405. Though illustratedseparately, in some embodiments the modeling system 420 and navigationfusion system 400 can be combined into a single system. The modelingsystem 420 includes a number of processing modules including modelgenerator 440 and feature extractor 450. Although the model datarepository 425 and depth features data repository 405 are illustratedwithin the navigation fusion system 400, these data repositories can insome implementations be located alternatively or additionally within themodeling system 420.

The model generator 440 is a module configured to receive data from amedical imaging system (not illustrated), for example, a CT imagingsystem or magnetic resonance imaging system. The received data caninclude a series of two-dimensional images representing the anatomicalluminal network of the patient. The model generator 440 can generate athree-dimensional volume of data from the series of two-dimensionalimages, and can form the virtual three-dimensional model of the internalsurfaces of the anatomical luminal network from the three-dimensionalvolume of data. For example, the model generator can apply segmentationto identify portions of the data corresponding to the tissue of theanatomical luminal network. As such, the resulting model can representthe interior surfaces of the tissue of the anatomical luminal network.

The model data repository 425 is a data storage device that stores datarepresenting a model of the luminal network of the patient, for example,the model generated by the model generator 440. Such a model can provide3D information about the structure and connectivity of the luminalnetwork, including the topography and/or diameters of patient airways insome examples. Some CT scans of patient lungs are performed atbreath-hold so that the patient's airways are expanded to their fulldiameter in the model.

The endoscope imaging data repository 480 is a data storage device thatstores image data received from a camera at a distal end of anendoscope, for example, the imaging device 315. The image data can bediscrete images or series of image frames in a video sequence in variousembodiments.

The feature extractor 450 is a module configured to receive the modelfrom the model generator 440 and build a database of depth featurescorresponding to a number of different location within the model. Forexample, the feature extractor 450 can identify a number of differentlocations within the model, computationally position a virtual imagingdevice at each of the locations, generate a virtual image at eachlocation, and then derive specified features from the virtual image. A“virtual imaging device” as described herein is not a physical imagingdevice, but rather a computational simulation of an image capturedevice. The simulation can generate virtual images based on virtualimaging device parameters including field of view, lens distortion,focal length, and brightness shading, which can in turn be based onparameters of an actual imaging device.

Each generated virtual image can correspond to a virtual depth maprepresenting the distance between the location of the virtual imagingdevice and tissue of the virtual luminal network within the virtualfield of view of the virtual imaging device. The feature extractor 450can match the virtual imaging device parameters to the parameters of anactual imaging device that has been identified for use in a medicalprocedure involving the patient's luminal network. An example processfor building the database is described in more detail below with respectto FIG. 20 .

The feature extractor 450 can also receive data from the endoscopeimaging data repository 480, generate a depth map representing thedistance between the endoscope imaging device and the imaged tissuerepresented by pixels of the image, and derive features from thegenerated depth map. In some embodiments, the feature extractor 450 canuse photoclinometry (e.g., shape by shading) processing to generate adepth map based on a single image. In some embodiments, the featureextractor 450 can use a stereoscopic image set depicting the imagedregion to generate a depth map.

Depth features data repository 405 is a data storage device that storesa database of features derived from depth maps and/or virtual depthmaps, as generated by the feature extractor 450. The features can varybased on the nature of the luminal network and/or the use of thefeatures during the navigation procedure. The features can include, forexample, positions of local maxima within the depth map (e.g.,representing the farthest virtual tissue visible down a branch of anairway), positions along a curve peak surrounding a local maxima, avalue representing the distance (e.g., number of pixels between)separating two local maxima, and/or the size, shape, and orientation ofa line or polygon connecting a number of local maxima. A curve peakrepresents a region in the depth map at which depth values of pixels onone side of the curve peak are increasing while depth values on theother side of the curve peak are decreasing. The curve peak can includea local maximum where the depth associated with a pixel is greater thandepths associated with pixels on either side of the pixel. The depthfeatures data repository 405 can store the features and associatedlocations within the virtual luminal network as a tuple, for example, inthe following form—{location_(n), feature value}— for each identifiedlocation. As an example, when the location relates to position within anairway and the feature relates to the distance between two identifiedlocal maxima, the tuple can be generated as {location_(n) (airwaysegment, depth within airway segment), feature value (distance)}. Assuch, the extracted features in the database can be quicklyprogrammatically evaluated in comparison to features extracted inreal-time from images of a navigated anatomical luminal network, and alocation corresponding to an identified best or close feature match canbe quickly ascertained.

The depth-based position estimator 410 is a module configured to comparethe feature(s) extracted in real-time from images of the anatomicalluminal network to the pre-computed feature(s) extracted from virtualimages. The depth-based position estimator 410 can scan the depthfeatures data repository 405 for a match of a virtual feature to thefeature extracted from an actual image, and can use the locationcorresponding to the match as the position of the instrument (e.g., anendoscope) within the anatomical luminal network. The match can be anexact match, the best match among the available features in the depthfeatures data repository 405, a match within a threshold difference fromthe extracted feature. The depth-based position estimator 410 can outputthe position to the state estimator 440, for example, for use as aninitial position (a “prior”) in a probabilistic evaluation of theposition of the instrument, or for use as a prior after occurrence of anadverse event (e.g., coughing) in which the precise location of theinstrument becomes unknown. The depth-based position estimator 410 canoutput the position to the registration calculator 465 for use ingenerating an initial registration between the model and an EM fielddisposed around the patient and/or an updated registration.

The endoscope EM sensor data repository 415 is a data storage devicethat stores data derived from an EM sensor at the distal end of anendoscope. As described above, such a sensor could include EM sensor125, and EM sensor coils 305 and the resulting data can be used toidentify position and orientation of the sensor within the EM field.Similar to the data from EM respiration sensors, data for an endoscopeEM sensor can be stored as a tuple in the form of {x, y, z, t_(n)} wherex, y, and z represent the coordinates of the sensor in the EM field attime t_(n). Some embodiments may further include roll, pitch, and yaw ofthe instrument in the EM sensor tuple. The endoscope EM sensor datarepository 415 can store a number of such tuples for eachendoscope-based sensor corresponding to a number of different times.

The registration calculator 465 is a module that can identify aregistration or mapping between the coordinate frame of the 3D model(e.g., a coordinate frame of the CT scanner used to generate the model)and the coordinate frame of the EM field (e.g., of the EM fieldgenerator 120). In order to track a sensor through the patient'sanatomy, the navigation fusion system 400 may require a process known as“registration,” by which the registration calculator 465 finds thegeometric transformation that aligns a single object between differentcoordinate systems. For instance, a specific anatomical site on apatient may have a representation in the 3D model coordinates and alsoin the EM sensor coordinates. In order to calculate an initialregistration, one implementation of the registration calculator 465 canperform registration as described in U.S. application Ser. No.15/268,238, filed Sep. 17, 2016, titled “Navigation of TubularNetworks,” the disclosure of which is hereby incorporated by reference.As an example of one possible registration technique, the registrationcalculator 465 can receive data from the endoscope imaging datarepository 480 and the EM sensor data repository 415 at a number ofdifferent points as the endoscope is inserted into the airways of thepatient, for example, as the endoscope reaches various bifurcations. Theimage data can be used to identify when the distal end of the endoscopehas reached a bifurcation, for example, via automated feature analysis.The registration calculator 465 can receive data from the endoscope EMsensor data repository 415 and identify a location of the EM sensor atthe distal end of the endoscope as the endoscope is positioned at thebifurcation. Some examples can use not only bifurcations but otherpoints in the patient's airway, and may map such points to correspondingpoints in a “skeleton” model of the airway. The registration calculator465 can use data linking at least three of EM positions to points in themodel in order to identify the geometric transformation between the EMfield and the model. Another embodiment can involve manual registration,for example, by taking at least 3 from a first bifurcation of thepatient's airway and from two more bifurcations in the left and rightlungs, and can use the corresponding points to calculate theregistration. This data to perform the geometric transformation (alsoreferred to as registration data) can be stored in the registration datarepository 475 as registration data.

After the initial registration is determined, the registrationcalculator 465 may update its estimate of the registration transformbased on received data so as to increase transform accuracy as well asto compensate for changes to the navigation system, e.g., changes due tomovement of the patient. In some aspects, the registration calculator465 may update the estimate of the registration transform continually,at defined intervals, and/or based on the position of the endoscope (orcomponent(s) thereof) in the luminal network.

Registration data repository 475 is a data storage device that storesthe registration data that, as just discussed, is usable to perform ageometric transformation from the coordinate frame of the EM field tothe coordinate frame of the model. Also discussed above, theregistration data may be generated by the registration calculator 465and may be updated continually or periodically in some implementations.

The location calculator 430 is a module that receives data from themodel data repository 425, registration data repository 475, and thescope position estimator 420 to translate EM sensor coordinates into 3Dmodel coordinates. The scope position estimator 420 calculates aninitial position of the EM sensor relative to the position of the EMfield generator, as described above. This position also corresponds to alocation within the 3D model. In order to translate the initial positionof the EM sensor from the EM coordinate frame into the model coordinateframe, the location calculator 430 can access the mapping between the EMcoordinate frame and the model coordinate frame (e.g., registrationdata) as stored in the registration data repository 475. In order totranslate the scope position into the 3D model coordinate frame, thelocation calculator 430 receives, as input, data representing thetopography of the 3D model from the model data repository 425, datarepresenting the registration between the EM field and the coordinateframe of the 3D model from the registration data repository 475, and theposition of the scope in the EM field from the scope position estimator420. Some embodiments can also receive prior estimated state data fromthe state estimator 440. Based on the received data, the locationcalculator 430 may perform, e.g., on-the-fly transformation of the EMsensor position data to a position in the 3D model. This can represent apreliminary estimate of the position of the distal end of the scopewithin the topography of the 3D model and can be provided as one inputto the state estimator 440 for generating a final estimate of the scopeposition, as described in more detail below.

The image analyzer 435 is a module that receives data from the endoscopeimaging data repository 480 and model data repository 425 and cancompare this data to determine endoscope positioning. For example, theimage analyzer 435 can access volume-rendered or surface-renderedendoluminal images of the airway tree from the model scans and cancompare the rendered images with the real-time image or video framesfrom the imaging device 315. For example, the images can be registered(e.g., using Powell's optimization, simplex or gradient methods,gradient descent algorithms with normalized cross correlation or mutualinformation as costs), and then weighted normalized sum of squaredifference errors and normalized mutual information can be used forcomparing the registered images obtained from the two sources.Similarity between a 2D image from the scan and a 2D image received fromthe endoscope can indicate that the endoscope is positioned near thelocation of the image from the scan. Such image-based navigation canperform local registrations at bifurcations of patient airways and socan be less susceptible to noise due to patient breathing motion than EMtracking systems. However, as the image analyzer 435 relies on theendoscope video, the analysis can be affected by artifacts in the imagescaused by patient coughing or mucous obstruction.

The image analyzer 435 can implement object recognition techniques insome embodiments, by which the image analyzer 435 can detect objectspresent in the field of view of the image data, such as branch openings,lesions, or particles. Using object recognition, the image analyzer canoutput object data indicating information about what objects wereidentified, as well as positions, orientations, and/or sizes of objectsrepresented as probabilities. As one example, object recognition can beused to detect objects that may indicate branch points in a luminalnetwork and then determine their position, size, and/or orientation. Inone embodiment, in a given image within a luminal network, each branchwill typically appear as a dark, approximately elliptical region, andthese regions may be detected automatically by a processor, usingregion-detection algorithms such as maximally stable extremal regions(MSER) as objects. The image analyzer 435 can use light reflectiveintensity combined with other techniques to identify airways. Further,image analyzer 435 can further track detected objects across a set ofsequential image frames to detect which branch has been entered fromamong a set of possible branches in the luminal network.

The robotic position data repository 470 is a data storage device thatstores robotic position data received from medical robotic system 110,for example, data related to physical movement of the medical instrumentor part of the medical instrument (e.g., the instrument tip or distalend) by the medical robotic system 110 within the luminal network.Example robotic position data may include, e.g., command datainstructing the instrument tip to reach a specific anatomical siteand/or change its orientation (e.g., with a specific pitch, roll, yaw,insertion, and retraction for one or both of a leader and a sheath of anendoscopic instrument) within the luminal network, insertion datarepresenting insertion movement of the part of the medical instrument(e.g., the instrument tip or sheath), instrument driver data, andmechanical data representing mechanical movement of an elongate memberof the medical instrument, such as, for example, motion of one or morepull wires, tendons or shafts of the endoscope that drive the actualmovement of the endoscope within the luminal network.

The navigation path data repository 445 is a data storage device thatstores data representing a pre-planned navigation path through theluminal network to a target tissue site. Navigating to a particularpoint in a luminal network of a patient's body may require certain stepsto be taken pre-operatively in order to generate the information neededto create the 3D model of the tubular network and to determine anavigation path within it. As described above, a 3D model may begenerated of the topography and structure of the specific patient'sairways. A target can be selected, for example, a lesion to biopsy or aportion of organ tissue to repair surgically. In one embodiment, theuser is capable of selecting the location of the target by interfacingwith a computer display that can show the 3D model, such as by clickingwith a mouse or touching a touchscreen. In some embodiments, thenavigation path may be identified programmatically by analysis of themodel and an identified lesion site to derive a shortest navigation pathto the lesion. In some embodiments the path may be identified by aphysician, or an automatically-identified path may be modified by aphysician. The navigation path can identify a sequence of brancheswithin the luminal network to travel through so as to reach theidentified target.

The state estimator 440 is a module that receives inputs and performsanalysis of the inputs to determine a state of the medical instrument.For example, the state estimator 440 can receive, as inputs, data fromthe depth-based position estimator 410, location calculator 430, imageanalyzer 435, navigation path data repository 445, and robotic positiondata repository 470. The state estimator 440 can implement aprobabilistic analysis to determine a state and correspondingprobability of the medical instrument within the luminal network giventhe provided inputs. Estimated state can refer to one or more of (1) thex,y,z position of the instrument relative to a coordinate frame of amodel of the luminal network, (2) whether the instrument is located in acertain region of the model, for example, a particular airway branch,(3) pitch, roll, yaw, insertion, and/or retraction of the instrument,and (4) distance to target. The state estimator 440 can provide theestimated state of the instrument (or the distal tip of the instrument)as a function of time.

In some embodiments, the state estimator 440 can implement a Bayesianframework to determine the state and corresponding probability. Bayesianstatistical analysis starts with a belief, called a prior, and thenupdate that belief with observed data. The prior represents an estimateof what the Bayesian model parameters might be and can be represented asa parameterized distribution. The observed data can be gathered toobtain evidence about actual values of the parameters. The outcome ofBayesian analysis is called a posterior, and represents a probabilisticdistribution expressing events in terms of confidence. If further datais obtained the posterior can be treated as the prior and updated withthe new data. This process employs the Bayes rule, which indicates aconditional probability, for example, how likely is event A if event Bhappens.

With respect to the disclosed navigation fusion system 400, the stateestimator 440 can use previously estimated state data as the prior andcan use the inputs from the respiration frequency and/or phaseidentifier 410, scope position estimator 420, location calculator 430,image analyzer 435, navigation path data repository 445, and/or roboticposition data repository 470 as observed data. At the outset of aprocedure, the described vision-based initialization techniques can beused to estimate the initial depth and roll in the trachea, and thisestimate output from the depth-based position estimator 410 can be usedas the prior. The state estimator 440 can perform Bayesian statisticalanalysis of the prior and observed data to generate a posteriordistribution representing a probability and confidence value of each ofa number of possible states.

The “probability” of the “probability distribution”, as used herein,refers to a likelihood of an estimation of a possible location and/ororientation of the medical instrument being correct. For example,different probabilities may be calculated by one of the algorithmmodules indicating the relative likelihood that the medical instrumentis in one of several different possible branches within the luminalnetwork. In one embodiment, the type of probability distribution (e.g.,discrete distribution or continuous distribution) is chosen to matchfeatures of an estimated state (e.g., type of the estimated state, forexample, continuous position information vs. discrete branch choice). Asone example, estimated states for identifying which segment the medicalinstrument is in for a trifurcation may be represented by a discreteprobability distribution, and may include three discrete values of 20%,30% and 50% representing chance as being in the location inside each ofthe three branches as determined by one of the algorithm modules. Asanother example, the estimated state may include a roll angle of themedical instrument of 40±5 degrees and a segment depth of the instrumenttip within a branch may be is 4±1 mm, each represented by a Gaussiandistribution which is a type of continuous probability distribution.

In contrast, the “confidence value,” as used herein, reflects a measureof confidence in the estimation of the state provided by one of themodules of FIG. 19 based one or more factors. For the EM-based modules,factors such as distortion to EM Field, inaccuracy in EM registration,shift or movement of the patient, and respiration of the patient mayaffect the confidence in estimation of the state. Particularly, theconfidence value in estimation of the state provided by the EM-basedmodules may depend on the particular respiration cycle of the patient,movement of the patient or the EM field generators, and the locationwithin the anatomy where the instrument tip locates. For the imageanalyzer 435, examples factors that may affect the confidence value inestimation of the state include illumination condition for the locationwithin the anatomy where the images are captured, presence of fluid,tissue, or other obstructions against or in front of the optical sensorcapturing the images, respiration of the patient, condition of thetubular network of the patient itself (e.g., lung) such as the generalfluid inside the tubular network and occlusion of the tubular network,and specific operating techniques used in, e.g., navigating or imagecapturing.

For example, one factor may be that a particular algorithm has differinglevels of accuracy at different depths in a patient's lungs, such thatrelatively close to the airway opening, a particular algorithm may havea high confidence in its estimations of medical instrument location andorientation, but the further into the bottom of the lung the medicalinstrument travels that confidence value may drop. Generally, theconfidence value is based on one or more systemic factors relating tothe process by which a result is determined, whereas probability is arelative measure that arises when trying to determine the correct resultfrom multiple possibilities with a single algorithm based on underlyingdata.

As one example, a mathematical equation for calculating results of anestimated state represented by a discrete probability distribution(e.g., branch/segment identification for a trifurcation with threevalues of an estimated state involved) can be as follows:

S ₁ =C _(EM) *P _(1,EM) +C _(Image) *P _(1,Image) +C _(Robot) *P_(1,Robot);

S ₂ =C _(EM) *P _(2,EM) +C _(Image) *P _(2,Image) +C _(Robot) *P_(2,Robot);

S ₃ =C _(EM) *P _(3,EM) +C _(Image) *P _(3,Image) +C _(Robot) *P_(3,Robot)

In the example mathematical equation above, S_(i) (i=1, 2, 3) representspossible example values of an estimated state in a case where 3 possiblesegments are identified or present in the 3D model, C_(EM), C_(Image),and C_(Robot) represents confidence value corresponding to EM-basedalgorithm, image-based algorithm, and robot-based algorithm andP_(i,Em), P_(i,Image), and P_(i,Robot) represent the probabilities forsegment i. Because of the probabilistic nature of such a fusionalgorithm, respiration can be tracked over time and even predicted toovercome latency and outlier disturbances.

In some embodiments, confidence values for data from the roboticposition data 470, location calculator 435, and image analyzer 435 canbe adaptively determined based on the respiration phase from therespiration frequency and/or phase identifier 410. For example, roboticposition data and image data can be affected differently than EM sensordata by respiration motion. In some embodiments, vision data obtainedfrom the endoscope imaging data repository 430 can be used to detectcertain kinds of respiratory motion that are not detectable via sensorsexternal to the luminal network, for example, movement of an airway in acranial-caudal (backward-forward) motion that can be detected throughvision processing.

The navigation controller 460 is a module that receives data from thestate estimator 440 and the navigation path data repository 445 and usesthis data to guide further operation of the medical robotic system 110.For example, the navigation controller 460 can plot the estimated statealong a predetermined navigation path and can determine a next movement(e.g., extension/retraction distance, roll, actuation of pull wires orother actuating mechanisms) for the instrument to advance along thenavigation path. The navigation controller 460 can automatically controlthe instrument according to the determined next movement in someembodiments. In some embodiments the navigation controller 460 canoutput specific instrument movement instructions and/or instrumentdriver operation instructions for display to the user, such as by theworkstation 200. The navigation controller 460 can cause display ofside-by-side views of a slice of the 3D model at the estimated positionand of the real-time images received from the scope imaging datarepository 480 in some embodiments in order to facilitate user-guidednavigation.

4. Overview of Example Navigation Techniques

In accordance with one or more aspects of the present disclosure, FIG.20 depicts a flowchart of an example process 500 for generating anextracted virtual feature data set. In some embodiments, the process 500can be performed pre-operatively, that is, before the start of a medicalprocedure that uses the models generated by, and features extracted by,the process 500. The process 500 can be implemented in the modelingsystem 420 FIG. 19 , the control and sensor electronics 184 of FIG. 16C,and/or the console base 201 of FIG. 17 , or component(s) thereof. Thegraphical depictions within the flowchart of FIG. 20 are provided toillustrate and not limit the described blocks, and it will beappreciated that the visual representations of the depicted model 515,depth maps 532, 534, and associated features may or may not be generatedand displayed during the course of the process 500.

At block 510, model generator 440 can access image data representativeof a patient's anatomical luminal network and generate athree-dimensional model 515. For example, CT scans or MM scans cangenerate a number of images depicting two-dimensional cross-sections ofthe anatomical luminal network. The model generator 440 can segmentthese two-dimensional images to isolate or segment the tissue of theanatomical luminal network, and can then build a three-dimensional pointcloud of data based on the isolated tissue positions in the variousimages and based on the spatial relationship of the cross-sectionsdepicted in the images. The model generator 440 can generate the modelbased on this three-dimensional point cloud. The three-dimensional modelcan model the interior surfaces of the anatomical luminal network as avirtual luminal network. For example, the model 515 can be a segmentedmap of a patient's airways generated from CT scans in someimplementations. The model can be any two or three dimensionalrepresentation of the actual luminal network (or a portion of theluminal network) of the patient.

At block 520, the feature extractor 450 can identify a number of virtuallocations 525 within the model 515. As one example, the featureextractor 450 can identify a number of locations within a tracheasegment of a model representing patient airways, for example, a hundredand twenty locations, or greater or fewer locations depending on theparameters of the navigation system 400. In other examples the featureextractor 450 can identify locations within other segments of an airwaymodel, for instance locations along a planned navigation path throughthe airway model, along the planned navigation path and branches withina predetermined proximity to the planned navigation path, or throughoutsome or all of the airway segments.

At block 530, the feature extractor 450 can generate a number of virtualdepth maps 532, 534 corresponding to the identified locations. Forexample, the feature extractor 450 can use the identified locations toset locations of a virtual imaging device within the virtual anatomicalluminal network, and can generate a virtual depth map 532, 534 for eachidentified location. The depicted example virtual depth map 532 andvirtual depth map 534 depict different representations of the same depthinformation relating to a virtual representation of a main carina 156 inan airway. Each virtual pixel in the two-dimensional representation ofvirtual depth map 532 is depicted with a color corresponding to itsdepth value, while the three-dimensional representation of virtual depthmap 534 depicts a dual-peak shape where each virtual pixel is shown at aheight along a z-axis corresponding to its depth value. The depictedvirtual depth maps are provided to illustrated the concepts of block530, however in some implementations of the process 500 no such visualrepresentations may be generated, as the process 500 may only requirethe data representing such depth maps in order to derive features asdescribed below.

In some implementations, at block 530 the feature extractor 450 canaccess parameters of an imaging device identified for use during amedical procedure during which the luminal network will be navigated,for example, imaging device 315 at the distal end of an endoscope. Thefeature extractor 450 can set virtual parameters of the virtual imagingdevice to match the parameters of the imaging device. Such parameterscan include field of view, lens distortion, focal length, and brightnessshading, and can be based on calibration data or data obtained bytesting the imaging device. Brightness shading, also known asvignetting, is a position dependent variation in the amount of lighttransmitted by an optical system causing darkening of an image near theedges. Vignetting results in a decrease in the amount of lighttransmitted by an optical system near the periphery of the lensfield-of-view (FOV), causing gradual darkening of an image at the edges.Vignetting can be corrected after image capture by calibrating a lensroll off distortion function of the camera. By matching the virtualparameters to the actual parameters, the resulting virtual depth maps532, 534 may more closely correspond to actual depth maps generatedbased on images captured by the imaging device.

At block 540, the feature extractor 450 analyzes the values of thevirtual depth maps in order to identify one or more depth criteria. Adepth criterion can be, for example, the position of a local maximawithin the depth map (e.g., a pixel representing the farthest virtualtissue visible down a branch of an virtual airway model) or any positionwithin a threshold distance from the local maxima along a curve peaksurrounding the local maxima. The described depth criterion positionscan be virtual pixel locations within the virtual depth map.

Block 540 provides a visual illustration of example depth criterion 522and 544 as local maxima, corresponding to the most distant virtualtissue visible by the virtual camera within the virtual left bronchusand virtual right bronchus. As a general rule, due to the typical shapeof the human lungs, a camera or virtual camera positioned near the maincarina will be able to see farther into the right bronchus than the leftbronchus. Accordingly, the depth criterion 544 corresponds to the mostdistant depicted virtual tissue within the right bronchus as it has agreater value than depth criterion 542, and the depth criterion 542corresponds to the most distant depicted virtual tissue within the leftbronchus. Such information can assist in identifying roll as describedherein.

At block 550, the feature extractor 450 derives a pre-identified virtualfeature from the identified depth criteria. For example, as shown thefeature extractor 450 can identify the value of the distance 555separating the depth criteria 542, 544. The distance value can berepresented as a number of pixels in an (x,y) space corresponding to atwo-dimensional depth map 532 or an (x,y,z) vector corresponding to thethree-dimensional depth map 544. The feature extractor 450 canadditionally or alternatively derive the identification and positioningof the right and left bronchus as the feature(s). In otherimplementations, for example, involving depth maps at locations thatview branchings of three or more airways, the feature can include thesize, shape, and orientation of a polygon connecting three or more localmaxima.

At block 560, the feature extractor 450 can generate a database of thevirtual location(s) and associated extracted virtual feature(s). Thisdatabase can be provided to navigation system 400 for use in calculatingreal-time instrument position determinations, for example, toautomatically initialize a probabilistic state estimation, calculateregistrations, and perform other navigation-related calculations.

FIG. 21 depicts a flowchart of an example intra-operative process 600for generating depth information based on captured endoscopic images andcalculated correspondence between features of the depth information withthe extracted virtual feature data set of FIG. 20 . The process 600 canbe implemented by the modeling system 420 and/or navigation fusionsystem 400 FIG. 19 , the control and sensor electronics 184 of FIG. 16C,and/or the console base 201 of FIG. 17 , or component(s) thereof.

At block 610, the feature extractor 450 receives imaging data capturedby an imaging device at the distal end of an instrument positionedwithin a patient's anatomical luminal network. For example, the imagingdevice can be imaging device 315 described above. An example visualrepresentation of the imaging data is shown by image 615 depicting themain carina of patient airways. The image 615 depicts the anatomicalmain carina corresponding to the virtual main carina represented by thevirtual depth map 532 of FIG. 20 . Image 615 depicts specific featuresusing a specific visual representation, and image 615 is provided toillustrate and not limit the process 600. Image 615 is representative ofendoluminal image data suitable for use in the process 600, and othersuitable image data can represent other anatomical structures and/or bedepicted as images using different visual representations. Further, someembodiments of the process 600 may operate on imaging data (e.g., valuesof pixels received from an image sensor of the imaging device) withoutgenerating a corresponding visible representation of the image data(e.g., image 615).

At block 620, the feature extractor 450 generates a depth map 620corresponding to the imaging data represented by image 615. Featureextractor 450 can calculate, for each pixel of the imaging data, a depthvalue representing an estimated distance between the imaging device anda tissue surface within the anatomical luminal network represented thatis corresponding to the pixel. Specifically, the depth value canrepresent an estimate of the physical distance between an entrance pupilof the imaging device's optical system and the imaged tissue depicted bythe pixel. In some embodiments, the feature extractor 450 can usephotoclinometry (e.g., shape by shading) processing to generate a depthmap based on a single image 615. By using photoclinometry, the featureextractor 450 can be robust to outliers due to reflectance differencesbetween portions of the tissue that may be covered in fluid (e.g.mucous). In some embodiments, the feature extractor 450 can use astereoscopic image set depicting the imaged region to generate a depthmap. For example, a robotically-controlled endoscope can capture a firstimage at a first location, be robotically retracted, extended, and/orturned a known distance to a second location, and can capture secondimage at the second location. The feature extractor 450 can use theknown translation of the robotically-controlled endoscope and thedisparity between the first and second images to generate the depth map.

At block 630, the feature extractor 450 identifies one or more depthcriteria in the depth map. As described above with respect to thevirtual depth maps, a depth criterion in a depth map generated based onreal image data can be, for example, the position of a local maximawithin the depth map (e.g., a pixel representing the farthest anatomicaltissue visible down a branch of an airway of the patient) or anyposition within a threshold distance from the local maxima along a curvepeak surrounding the local maxima. The described depth criterionpositions can be pixel locations within the image 615. The depthcriterion selected for identification at block 630 preferablycorresponds to the depth criterion identified at block 540.

For example, feature extractor 450 can identify a first pixel of theplurality of pixels corresponding to a first depth criterion in thedepth map and a second pixel of the plurality of pixels corresponding toa second depth criterion in the depth map, and in some embodiments eachdepth criterion can correspond to a local maximum in a region of depthvalues around the identified pixel. Block 630 provides a visualillustration of example depth criterion 632 and 634 as local maxima,corresponding to the most distant tissue within the left bronchus andright bronchus that is visible by the imaging device 315. Specifically,depth criterion 634 corresponds to a pixel representing the most distantimaged tissue within the right bronchus as it has a greater value thandepth criterion 632, and the depth criterion 632 corresponds a pixelrepresenting to the most distant imaged tissue within the left bronchus.Other airway bifurcations can have similar known depth relationshipsbetween different branches.

At block 640, the feature extractor 450 derives a pre-identified featurefrom the identified depth criteria. For example, as shown the featureextractor 450 can calculate the value of the distance 645 (e.g.,quantity of separation) between the pixels corresponding to depthcriteria 632 and 634. The distance value can be represented as a numberof pixels in an (x,y) space corresponding to a two-dimensional depth mapor an (x,y,z) vector corresponding to the three-dimensional depth map625, preferably in the same format as the feature identified at block550 of process 500. The feature extractor 450 can additionally oralternatively derive the identification and positioning of the right andleft bronchus as the feature(s). In other implementations, for example,involving depth maps at locations that view branchings of three or moreairways, the feature can include the size, shape, and orientation of apolygon connecting three or more local maxima.

At block 650, the depth-based position estimator 410 calculates acorrespondence between the feature(s) derived from the imaging data anda number of features in depth features data repository 405. For example,the feature derived from the imaging data can be the value of distance645 calculated based on the identified depth criteria of the depth map625, as described with respect to block 640. The depth-based positionestimator 410 can compare the value of distance 645 to distance valuesassociated with a number of locations in the trachea to identify one ofthe distance values that corresponds to the value of distance 645. Thesedistance values can be pre-computed and stored in data repository 405 asdescribed with respect to FIG. 20 above, or can be computed in real-timeas the patient's anatomy is being navigated. Values computed in realtime may be stored in a working memory during correspondencecalculations, or may be added to the data repository 405 and then lateraccessed if the location corresponding to the value is involved inadditional correspondence calculations.

To determine the correspondence, the depth-based position estimator 410can identify the value of distance 555 (discussed above with respect toblock 550 of process 500) as an exact match to the value of distance645, as the best match (e.g., closest value) to the value of distance645 among the options in the depth features data repository 405, or asthe first match within a predetermined threshold of the value ofdistance 645. It will be appreciated that the navigation system 400 canbe preconfigured to look for an exact match, best match, or firstwithin-threshold match, or to dynamically look for one of these optionsbased on current navigation conditions, based on a tradeoff betweencomputation speed and accuracy of the position output.

At block 660, the depth-based position estimator 410 determines anestimated pose of the distal end of the instrument within the anatomicalluminal network based on the virtual location associated with thevirtual feature that was identified in the correspondence calculationsof block 650. The pose can include the position of the instrument (e.g.,insertion depth within a segment of an airway or other luminal networkportion), the roll, pitch, and/or yaw of the instrument, or otherdegrees of freedom. As described above, the depth features datarepository 405 can store a database of tuples or associated valuesincluding locations and the feature(s) extracted from virtual imagesgenerated at the locations. Accordingly, at block 660 the depth-basedposition estimator 410 can access the location information stored inassociation with the feature identified at block 650 and output thislocation as the position of the instrument. In some embodiments, block660 can include identifying an angular transformation between thepositions of the right and left bronchus in image 615 and the virtualpositions of the virtual right and left bronchus in virtual depth map532. The angular transformation can be used to determine the roll of theinstrument within the airway.

At block 670, the depth-based position estimator 410 outputs theidentified pose for use in the navigation system 400. As describedabove, the pose can be output to the state estimator 440 and used as anautomatically-determined Bayesian prior during initialization, asopposed to an initialization process that requires the user toreposition the endoscope at a number of specified locations in order toenable the initialization. In some embodiments, the pose can be outputto the registration calculator 465 for use in calculating a registrationbetween the model coordinate frame and the EM coordinate frame.Beneficially, the processes 500 and 600 enable such calculations withoutrequiring the physician to deviate from a pre-determined navigation paththrough the patient's airways to the target tissue site.

5. Alternatives

Several alternatives of the subject matter described herein are providedbelow.

1. A method of facilitating navigation of an anatomical luminal networkof a patient, the method, executed by a set of one or more computingdevices, comprising:

-   -   receiving imaging data captured by an imaging device at a distal        end of an instrument positioned within the anatomical luminal        network;    -   accessing a virtual feature derived from a virtual image        simulated from a viewpoint of a virtual imaging device        positioned at a virtual location within a virtual luminal        network representative of the anatomical luminal network;    -   calculating a correspondence between a feature derived from the        imaging data and the virtual feature derived from the virtual        image; and    -   determining a pose of the distal end of the instrument within        the anatomical luminal network based on the virtual location        associated with the virtual feature.

2. The method of alternative 1, further comprising generating a depthmap based on the imaging data, wherein the virtual feature is derivedfrom a virtual depth map associated with the virtual image, and whereincalculating the correspondence is based at least partly on correlatingone or more features of the depth map and one or more features of thevirtual depth map.

3. The method of alternative 2, further comprising:

-   -   generating the depth map by calculating, for each pixel of a        plurality of pixels of the imaging data, a depth value        representing an estimated distance between the imaging device        and a tissue surface within the anatomical luminal network        corresponding to the pixel;    -   identifying a first pixel of the plurality of pixels        corresponding to a first depth criterion in the depth map and a        second pixel of the plurality of pixels corresponding to a        second depth criterion in the depth map;    -   calculating a first value representing a distance between the        first and second pixels;    -   wherein the virtual depth map comprises, for each virtual pixel        of a plurality of virtual pixels, a virtual depth value        representing a virtual distance between the virtual imaging        device and a portion of the virtual luminal network represented        by the virtual pixel, and wherein accessing the virtual feature        derived from the virtual image comprises accessing a second        value representing a distance between first and second depth        criteria in the virtual depth map; and    -   calculating the correspondence based on comparing the first        value to the second value.

4. The method of alternative 3, further comprising:

-   -   accessing a plurality of values representing distances between        first and second depth criteria in a plurality of virtual depth        maps each representing a different one of a plurality of virtual        locations within the virtual luminal network; and    -   calculating the correspondence based on the second value        corresponding more closely to the first value than other values        of the plurality of values.

5. The method of any one of alternatives 3 or 4, wherein the anatomicalluminal network comprises airways and the imaging data depicts abifurcation of the airways, the method further comprising:

-   -   identifying one of the first and second depth criteria as a        right bronchus in each of the depth map and the virtual depth        map; and    -   determining a roll of the instrument based on an angular        distance between a first position of the right bronchus in the        depth map and a second position of the right bronchus in the        virtual depth map, wherein the pose of the distal end of the        instrument within the anatomical luminal network comprises the        determined roll.

6. The method of any of alternatives 2-5, further comprising:

-   -   identifying three or more depth criteria in each of the depth        map and the virtual depth map;    -   determining a shape and location of a polygon connecting the        depth criteria in each of the depth map and the virtual depth        map; and    -   calculating the correspondence based on comparing the shape and        location of the polygon of the depth map to the shape and        location of the polygon of the virtual depth map.

7. The method of any of alternatives 2-6, wherein generating the depthmap is based on photoclinometry.

8. The method of any of alternatives 1-7, further comprising:

-   -   calculating a probabilistic state of the instrument within the        anatomical luminal network based on a plurality of inputs        comprising the position; and    -   guiding navigation of the instrument through the anatomical        luminal network based at least partly on the probabilistic        state.

9. The method of alternative 8, further comprising initializing anavigation system configured to calculate the probabilistic state andguide the navigation of the anatomical luminal network based on theprobabilistic state, wherein the initializing of the navigation systemcomprises setting a prior of a probability calculator based on theposition.

10. The method of alternative 9, further comprising:

-   -   receiving additional data representing an updated pose of the        distal end of the instrument;    -   setting a likelihood function of the probability calculator        based on the additional data; and    -   determining the probabilistic state using the probability        calculator based on the prior and the likelihood function.

11. The method of any one of alternatives 8-10, further comprising:

-   -   providing the plurality of inputs to a navigation system        configured to calculate the probabilistic state, a first input        comprising the pose of the distal end of the instrument and at        least one additional input comprising one or both of robotic        position data from a robotic system actuating movement of the        instrument and data received from a position sensor at the        distal end of the instrument; and    -   calculating the probabilistic state of the instrument based on        the first input and the at least one additional input.

12. The method of any one of alternatives 1-11, further comprisingdetermining a registration between a coordinate frame of the virtualluminal network and a coordinate frame of an electromagnetic fieldgenerated around the anatomical luminal network based at least partly onthe pose of the distal end of the instrument within the anatomicalluminal network determined based on the calculated correspondence.

13. The method of any one of alternatives 1-12, wherein determining theposition comprises determining a distance that the distal end of theinstrument is advanced within a segment of the anatomical luminalnetwork.

14. A system configured to facilitate navigation of an anatomicalluminal network of a patient, the system comprising:

-   -   an imaging device at a distal end of an instrument;    -   at least one computer-readable memory having stored thereon        executable instructions; and    -   one or more processors in communication with the at least one        computer-readable memory and configured to execute the        instructions to cause the system to at least:        -   receive imaging data captured by the imaging device with the            distal end of the instrument positioned within the            anatomical luminal network;        -   access a virtual feature derived from a virtual image            simulated from a viewpoint of a virtual imaging device            positioned at a virtual location within a virtual luminal            network representative of the anatomical luminal network;        -   calculate a correspondence between a feature derived from            the imaging data and the virtual feature derived from the            virtual image; and        -   determine a pose of the distal end of the instrument            relative within the anatomical luminal network based on the            virtual location associated with the virtual feature.

15. The system of alternative 14, wherein the one or more processors areconfigured to execute the instructions to cause the system to at least:

-   -   generate a depth map based on the imaging data, wherein the        virtual image represents a virtual depth map; and    -   determine the correspondence based at least partly on        correlating one or more features of the depth map and one or        more features of the virtual depth map.

16. The system of alternative 15, wherein the one or more processors areconfigured to execute the instructions to cause the system to at least:

-   -   generate the depth map by calculating, for each pixel of a        plurality of pixels of the imaging data, a depth value        representing an estimated distance between the imaging device        and a tissue surface within the anatomical luminal network        corresponding to the pixel;    -   identify a first pixel of the plurality of pixels corresponding        to a first depth criterion in the depth map and a second pixel        of the plurality of pixels corresponding to a second depth        criterion in the depth map;    -   calculate a first value representing a distance between the        first and second pixels;    -   wherein the virtual depth map comprises, for each virtual pixel        of a plurality of virtual pixels, a virtual depth value        representing a virtual distance between the virtual imaging        device and a portion of the virtual luminal network represented        by the virtual pixel, and wherein the feature derived from the        virtual image comprises a second value representing a distance        between first and second depth criteria in the virtual depth        map; and    -   determine the correspondence based on comparing the first value        to the second value.

17. The system of alternative 16, wherein the one or more processors areconfigured to execute the instructions to cause the system to at least:

-   -   access a plurality of values representing distances between        first and second depth criteria in a plurality of virtual depth        maps each representing a different one of a plurality of virtual        locations within the virtual luminal network; and    -   calculate the correspondence based on the second value        corresponding more closely to the first value than other values        of the plurality of values identify the second value as a        closest match to the first value among the plurality of values.

18. The system of any one of alternatives 16-17, wherein the anatomicalluminal network comprises airways and the imaging data depicts abifurcation of the airways, wherein the one or more processors areconfigured to execute the instructions to cause the system to at least:

-   -   identify one of the first and second depth criteria as a right        bronchus in each of the depth map and the virtual depth map; and    -   determine a roll of the instrument based on an angular distance        between a first position of the right bronchus in the depth map        and a second position of the right bronchus in the virtual depth        map, wherein the pose of the distal end of the instrument within        the anatomical luminal network comprises the determined roll.

19. The system of any one of alternatives 15-18, wherein the one or moreprocessors are configured to execute the instructions to cause thesystem to at least:

-   -   identify three or more depth criteria in each of the depth map        and the virtual depth map;    -   determine a shape and location of a polygon connecting the three        or more depth criteria in each of the depth map and the virtual        depth map; and    -   calculate the correspondence based on comparing the shape and        location of the polygon of the depth map to the shape and        location of the polygon of the virtual depth map.

20. The system of any one of alternatives 15-19, wherein the one or moreprocessors are configured to execute the instructions to cause thesystem to at least generate the depth map based on photoclinometry.

21. The system of any one of alternatives 14-20, wherein the one or moreprocessors are configured to communicate with a navigation system, andwherein the one or more processors are configured to execute theinstructions to cause the system to at least:

-   -   calculate a probabilistic state of the instrument within the        anatomical luminal network using the navigation system based at        least partly on a plurality of inputs comprising the position;        and    -   guide navigation of the instrument through the anatomical        luminal network based at least partly on the probabilistic state        calculated by the navigation system.

22. The system of alternative 21, further comprising a robotic systemconfigured to guide movements of the instrument during the navigation.

23. The system of alternative 22, wherein the plurality of inputscomprise robotic position data received from the robotic system, andwherein the one or more processors are configured to execute theinstructions to cause the system to at least calculate the probabilisticstate of the instrument using the navigation system based at leastpartly on the position and on the robotic position data.

24. The system of any one of alternatives 21-23, further comprising aposition sensor at the distal end of an instrument, the plurality ofinputs comprise data received from the position sensor, and wherein theone or more processors are configured to execute the instructions tocause the system to at least calculate the probabilistic state of theinstrument using the navigation system based at least partly on theposition and on the data received from the position sensor.

25. The system of any one of alternatives 14-24, wherein the one or moreprocessors are configured to execute the instructions to cause thesystem to at least determine a registration between a coordinate frameof the virtual luminal network and a coordinate frame of anelectromagnetic field generated around the anatomical luminal networkbased at least partly on the position.

26. A non-transitory computer readable storage medium having storedthereon instructions that, when executed, cause at least one computingdevice to at least:

-   -   access a virtual three-dimensional model of internal surfaces of        an anatomical luminal network of a patient;    -   identify a plurality of virtual locations within the virtual        three-dimensional model;    -   for each virtual location of the plurality of virtual locations        within the virtual three-dimensional model:        -   generate a virtual depth map representing virtual distances            between a virtual imaging device positioned at the virtual            location and a portion of the internal surfaces within a            field of view of the virtual imaging device when positioned            at the virtual location, and        -   derive at least one virtual feature from the virtual depth            map; and generate a database associating the plurality of            virtual locations with the at least one virtual feature            derived from the corresponding virtual depth map.

27. The non-transitory computer readable storage medium of alternative26, wherein the instructions, when executed, cause the at least onecomputing device to at least provide the database to a navigation systemconfigured to guide navigation of an instrument through the anatomicalluminal network during a medical procedure.

28. The non-transitory computer readable storage medium of alternative27, wherein the instructions, when executed, cause the at least onecomputing device to at least:

-   -   access data representing an imaging device positioned at a        distal end of the instrument;    -   identify image capture parameters of the imaging device; and    -   set virtual image capture parameters of the virtual imaging        device to correspond to the image capture parameters of the        imaging device.

29. The non-transitory computer readable storage medium of alternative28, wherein the instructions, when executed, cause the at least onecomputing device to at least generate the virtual depth maps based onthe virtual image capture parameters.

30. The non-transitory computer readable storage medium of any one ofalternatives 28-29, wherein the image capture parameters comprise one ormore of field of view, lens distortion, focal length, and brightnessshading.

31. The non-transitory computer readable storage medium of any one ofalternatives 26-30, wherein the instructions, when executed, cause theat least one computing device to at least:

-   -   for each virtual location of the plurality of virtual locations:        -   identify first and second depth criteria in the virtual            depth map, and        -   calculate a value representing a distance between the first            and second depth criteria; and    -   create the database by associating the plurality of virtual        locations with the corresponding value.

32. The non-transitory computer readable storage medium of any one ofalternatives 26-31, wherein the instructions, when executed, cause theat least one computing device to at least:

-   -   for each virtual location of the plurality of virtual locations:        -   identify three or more depth criteria in the virtual depth            map, and        -   determine a shape and location of a polygon connecting the            three or more depth criteria; and        -   create the database by associating the plurality of virtual            locations with the shape and location of the corresponding            polygon.

33. The non-transitory computer readable storage medium of any one ofalternatives 26-32, wherein the instructions, when executed, cause theat least one computing device to at least:

-   -   generate a three-dimensional volume of data from a series of        two-dimensional images representing the anatomical luminal        network of the patient; and    -   form the virtual three-dimensional model of the internal        surfaces of the anatomical luminal network from the        three-dimensional volume of data.

34. The non-transitory computer readable storage medium of alternative33, wherein the instructions, when executed, cause the at least onecomputing device to at least control a computed tomography imagingsystem to capture the series of two-dimensional images.

35. The non-transitory computer readable storage medium of any one ofalternatives 33-34, wherein the instructions, when executed, cause theat least one computing device to at least form the virtualthree-dimensional model by applying volume segmentation to thethree-dimensional volume of data.

36. A method of facilitating navigation of an anatomical luminal networkof a patient, the method, executed by a set of one or more computingdevices, comprising:

-   -   receiving a stereoscopic image set representing an interior of        the anatomical luminal network;    -   generating a depth map based on the stereoscopic image set;    -   accessing a virtual feature derived from a virtual image        simulated from a viewpoint of a virtual imaging device        positioned at a location within a virtual luminal network;    -   calculating a correspondence between a feature derived from the        depth map and the virtual feature derived from the virtual        image; and    -   determining a pose of the distal end of the instrument within        the anatomical luminal network based on the virtual location of        associated with the virtual feature.

37. The method of alternative 36, wherein generating the stereoscopicimage set comprises:

-   -   positioning an imaging device at a distal end of an instrument        at a first location within the anatomical luminal network;    -   capturing a first image of an interior of the anatomical luminal        network with the imaging device positioned at the first        location;    -   robotically controlling the imaging device to move a known        distance to a second location within the anatomical luminal        network; and    -   capturing a second image of the interior of the anatomical        luminal network with the imaging device positioned at the second        location.

38. The method of alternative 37, wherein robotically controlling theimaging device to move the known distance comprises one or both ofretracting the imaging device and angularly rolling the imaging device.

6. Implementing Systems and Terminology

Implementations disclosed herein provide systems, methods and apparatusfor improved navigation of luminal networks.

It should be noted that the terms “couple,” “coupling,” “coupled” orother variations of the word couple as used herein may indicate eitheran indirect connection or a direct connection. For example, if a firstcomponent is “coupled” to a second component, the first component may beeither indirectly connected to the second component via anothercomponent or directly connected to the second component.

The feature correspondence calculations, position estimation, androbotic motion actuation functions described herein may be stored as oneor more instructions on a processor-readable or computer-readablemedium. The term “computer-readable medium” refers to any availablemedium that can be accessed by a computer or processor. By way ofexample, and not limitation, such a medium may comprise RAM, ROM,EEPROM, flash memory, CD-ROM or other optical disk storage, magneticdisk storage or other magnetic storage devices, or any other medium thatcan be used to store desired program code in the form of instructions ordata structures and that can be accessed by a computer. It should benoted that a computer-readable medium may be tangible andnon-transitory. As used herein, the term “code” may refer to software,instructions, code or data that is/are executable by a computing deviceor processor.

The methods disclosed herein comprise one or more steps or actions forachieving the described method. The method steps and/or actions may beinterchanged with one another without departing from the scope of theclaims. In other words, unless a specific order of steps or actions isrequired for proper operation of the method that is being described, theorder and/or use of specific steps and/or actions may be modifiedwithout departing from the scope of the claims.

As used herein, the term “plurality” denotes two or more. For example, aplurality of components indicates two or more components. The term“determining” encompasses a wide variety of actions and, therefore,“determining” can include calculating, computing, processing, deriving,investigating, looking up (e.g., looking up in a table, a database oranother data structure), ascertaining and the like. Also, “determining”can include receiving (e.g., receiving information), accessing (e.g.,accessing data in a memory) and the like. Also, “determining” caninclude resolving, selecting, choosing, establishing and the like.

The phrase “based on” does not mean “based only on,” unless expresslyspecified otherwise. In other words, the phrase “based on” describesboth “based only on” and “based at least on.”

The previous description of the disclosed implementations is provided toenable any person skilled in the art to make or use the presentinvention. Various modifications to these implementations will bereadily apparent to those skilled in the art, and the generic principlesdefined herein may be applied to other implementations without departingfrom the scope of the invention. For example, it will be appreciatedthat one of ordinary skill in the art will be able to employ a numbercorresponding alternative and equivalent structural details, such asequivalent ways of fastening, mounting, coupling, or engaging toolcomponents, equivalent mechanisms for producing particular actuationmotions, and equivalent mechanisms for delivering electrical energy.Thus, the present invention is not intended to be limited to theimplementations shown herein but is to be accorded the widest scopeconsistent with the principles and novel features disclosed herein.

1. A method of facilitating navigation of an anatomical luminal networkof a patient, the method, executed by a set of one or more computingdevices, comprising: receiving imaging data captured by an imagingdevice at a distal end of an instrument positioned within the anatomicalluminal network; generating a depth map based on the imaging data;accessing a virtual depth map associated with a virtual image, thevirtual image simulated from a viewpoint of a virtual imaging devicepositioned at a virtual location within a virtual luminal networkrepresentative of the anatomical luminal network; identifying three ormore depth criteria in each of the depth map and the virtual depth map;determining a first shape connecting the three or more depth criteria inthe depth map and a second shape connecting the three or more depthcriteria in the virtual depth map; determining that the first shape andthe second shape match within a threshold difference; and determining apose of the distal end of the instrument within the anatomical luminalnetwork based on the determination.
 2. The method of claim 1, wherein:generating the depth map comprises determining, for a plurality ofpixels of the imaging data, depth values representing estimateddistances between the imaging device and a tissue surface within theanatomical luminal network corresponding to the pixels, accessing thevirtual depth map comprises accessing, for a plurality of virtual pixelsof the virtual image, virtual depth values representing virtualdistances between the virtual imaging device and a portion of thevirtual luminal network represented by the virtual pixels, identifyingthe three or more depth criteria in each of the depth map and thevirtual depth map comprises: identifying at least a first pixel of theplurality of pixels corresponding to a first depth criterion in thedepth map, a second pixel of the plurality of pixels corresponding to asecond depth criterion in the depth map, and a third pixel of theplurality of pixels corresponding to a third depth criterion, andaccessing at least a first virtual pixel of the plurality of virtualpixels corresponding to a first virtual depth criterion in the depthmap, a second virtual pixel of the plurality of virtual pixelscorresponding to a second virtual depth criterion in the depth map, anda third virtual pixel of the plurality of virtual pixels correspondingto a third virtual depth criterion; and determining the first shape andthe second shape comprises: determining the first shape as a firstpolygon connecting at least the first pixel, the second pixel, and thethird pixel, and determining the second shape comprises determining thesecond shape as a second polygon connecting at least the first virtualpixel, the second virtual pixel, and the third virtual pixel.
 3. Themethod of claim 2, wherein: the three or more depth criteria associatedwith the depth map corresponds to positions of local depth value extremawithin the depth map, and the three or more depth criteria associatedwith the virtual depth map corresponds to positions of local depth valueextrema within the virtual depth map.
 4. The method of claim 2, whereindetermining that the first shape and the second shape match within thethreshold difference comprises comparing at least one of: a first sizeof the first polygon and a second size of the second polygon, a firstlocation of the first polygon and a second location of the secondpolygon, or a first orientation of the first polygon and a secondorientation of the second polygon.
 5. The method of claim 1, whereingenerating the depth map is based on photoclinometry performed on asingle captured image.
 6. The method of claim 1, wherein generating thedepth map comprises: positioning the imaging device at a first locationwithin the anatomical luminal network; capturing a first image of a setof stereoscopic images with the imaging device positioned at the firstlocation; robotically controlling the imaging device to move a knowndistance to a second location within the anatomical luminal network; andcapturing a second image of the set of stereoscopic images with theimaging device positioned at the second location.
 7. The method of claim6, wherein robotically controlling the imaging device to move the knowndistance comprises one or both of retracting the imaging device andangularly rolling the imaging device.
 8. The method of claim 2, furthercomprising: generating a database associating a virtual location and thesecond shape derived from the corresponding virtual depth map, thevirtual location within a three-dimensional model of internal surfacesof the anatomical luminal network.
 9. The method of claim 8, wherein thedatabase further associates the virtual location with at least one of anairway segment or a depth within the airway segment.
 10. The method ofclaim 1, further comprising determining a registration between acoordinate frame of the virtual luminal network and a coordinate frameof an electromagnetic field generated around the anatomical luminalnetwork based at least in part on the pose of the distal end of theinstrument within the anatomical luminal network.
 11. The method ofclaim 1, wherein determining that the first shape and the second shapematch within a threshold difference comprises identifying an angulartransformation between the first shape and the second shape, and themethod further comprising: determining a roll associated with the distalend of the instrument within the anatomical luminal network based on theangular transformation.
 12. The method of claim 1, wherein determiningthe first shape and the second shape comprises: determining the firstshape comprises determining the first shape as a first curve peaksurrounding a first local depth value extrema within the depth map, anddetermining the second shape comprises determining the second shape as asecond curve peak surrounding a second local depth value extrema withinthe virtual depth map.
 13. A system configured to facilitate navigationof an anatomical luminal network of a patient, the system comprising: animaging device at a distal end of an instrument positioned within theanatomical luminal network; at least one computer-readable memory havingstored thereon executable instructions; and one or more processors incommunication with the at least one computer-readable memory andconfigured to execute the instructions to cause the system to at least:receive imaging data captured by the imaging device at the distal end ofthe instrument; generate a depth map based on the imaging data; access avirtual depth map associated with a virtual image, the virtual imagesimulated from a viewpoint of a virtual imaging device positioned at avirtual location within a virtual luminal network representative of theanatomical luminal network; identify three or more depth criteria ineach of the depth map and the virtual depth map; determine a first shapeconnecting the three or more depth criteria in the depth map and asecond shape connecting the three or more depth criteria in the virtualdepth map; determine that the first shape and the second shape matchwithin a threshold difference; and determine a pose of the distal end ofthe instrument within the anatomical luminal network based on thedetermination.
 14. The system of claim 13, wherein: generating the depthmap comprises determining, for a plurality of pixels of the imagingdata, depth values representing estimated distances between the imagingdevice and a tissue surface within the anatomical luminal networkcorresponding to the pixels, accessing the virtual depth map comprisesaccessing, for a plurality of virtual pixels of the virtual image,virtual depth values representing virtual distances between the virtualimaging device and a portion of the virtual luminal network representedby the virtual pixels, identifying the three or more depth criteria ineach of the depth map and the virtual depth map comprises: identifyingat least a first pixel of the plurality of pixels corresponding to afirst depth criterion in the depth map, a second pixel of the pluralityof pixels corresponding to a second depth criterion in the depth map,and a third pixel of the plurality of pixels corresponding to a thirddepth criterion, and accessing at least a first virtual pixel of theplurality of virtual pixels corresponding to a first virtual depthcriterion in the depth map, a second virtual pixel of the plurality ofvirtual pixels corresponding to a second virtual depth criterion in thedepth map, and a third virtual pixel of the plurality of virtual pixelscorresponding to a third virtual depth criterion; and determining thefirst shape and the second shape comprises: determining the first shapeas a first polygon connecting at least the first pixel, the secondpixel, and the third pixel, and determining the second shape comprisesdetermining the second shape as a second polygon connecting at least thefirst virtual pixel, the second virtual pixel, and the third virtualpixel.
 15. The system of claim 13, wherein: the three or more depthcriteria associated with the depth map corresponds to positions of localdepth value extrema within the depth map, and the three or more depthcriteria associated with the virtual depth map corresponds to positionsof local depth value extrema within the virtual depth map.
 16. Thesystem of claim 14, wherein determining that the first shape and thesecond shape match within the threshold difference comprises comparingat least one of: a first size of the first polygon and a second size ofthe second polygon, a first location of the first polygon and a secondlocation of the second polygon, or a first orientation of the firstpolygon and a second orientation of the second polygon.
 17. The systemof claim 13, wherein generating the depth map is based onphotoclinometry performed on a single captured image.
 18. The system ofclaim 13, wherein generating the depth map comprises: positioning theimaging device at a first location within the anatomical luminalnetwork; capturing a first image of a set of stereoscopic images withthe imaging device positioned at the first location; roboticallycontrolling the imaging device to move a known distance to a secondlocation within the anatomical luminal network; and capturing a secondimage of the set of stereoscopic images with the imaging devicepositioned at the second location.
 19. A non-transitory computerreadable storage medium having stored thereon instructions that, whenexecuted, cause at least one computing device to: receive imaging datacaptured by an imaging device at a distal end of an instrument; generatea depth map based on the imaging data; access a virtual depth mapassociated with a virtual image, the virtual image simulated from aviewpoint of a virtual imaging device positioned at a virtual locationwithin a virtual luminal network representative of an anatomical luminalnetwork; identify three or more depth criteria in each of the depth mapand the virtual depth map; determine a first shape connecting the threeor more depth criteria in the depth map and a second shape connectingthe three or more depth criteria in the virtual depth map; determinethat the first shape and the second shape match within a thresholddifference; and determine a pose of the distal end of the instrumentwithin the anatomical luminal network based on the determination. 20.The non-transitory computer readable storage medium of claim 19,wherein: generating the depth map comprises determining, for a pluralityof pixels of the imaging data, depth values representing estimateddistances between the imaging device and a tissue surface within theanatomical luminal network corresponding to the pixels, accessing thevirtual depth map comprises accessing, for a plurality of virtual pixelsof the virtual image, virtual depth values representing virtualdistances between the virtual imaging device and a portion of thevirtual luminal network represented by the virtual pixels, identifyingthe three or more depth criteria in each of the depth map and thevirtual depth map comprises: identifying at least a first pixel of theplurality of pixels corresponding to a first depth criterion in thedepth map, a second pixel of the plurality of pixels corresponding to asecond depth criterion in the depth map, and a third pixel of theplurality of pixels corresponding to a third depth criterion, andaccessing at least a first virtual pixel of the plurality of virtualpixels corresponding to a first virtual depth criterion in the depthmap, a second virtual pixel of the plurality of virtual pixelscorresponding to a second virtual depth criterion in the depth map, anda third virtual pixel of the plurality of virtual pixels correspondingto a third virtual depth criterion; and determining the first shape andthe second shape comprises: determining the first shape as a firstpolygon connecting at least the first pixel, the second pixel, and thethird pixel, and determining the second shape comprises determining thesecond shape as a second polygon connecting at least the first virtualpixel, the second virtual pixel, and the third virtual pixel.